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IoT Data Analytics at the Edge: Exploring the convergence of IoT, Data Analytics, and Edge Computing with Grafana, Mosquitto, and TimescaleDB on ARM-based devices
Posted by Gary A. Stafford in Analytics, AWS, Cloud, IoT, Python, Raspberry Pi, Software Development on April 15, 2021
This post is a revised version of an earlier post, featuring major version updates of TimescaleDB (v1.7.4-pg12 to v2.0.0-pg12), Grafana (v7.1.5 to v7.5.2), and Mosquitto (v1.6.12 to v2.0.9). All source code and SQL scripts are revised. Note that TimeScaleDB has a current limitation/bug with Docker on ARM later than v2.0.0.
The Edge
Edge computing is a fast-growing technology trend, which involves pushing compute capabilities to a network’s edge. Wikipedia describes edge computing as a distributed computing paradigm that brings computation and data storage closer to the location needed to improve response times and save bandwidth. The term edge commonly refers to a compute node at the edge of a network (edge device), sitting close to the data source and between that data source and external system such as the Cloud. In his recent post, 3 Advantages (And 1 Disadvantage) Of Edge Computing, well-known futurist Bernard Marr argues reduced bandwidth requirements, reduced latency, and enhanced security and privacy as three primary advantages of edge computing.
David Ricketts, Head of Marketing at Quiss Technology PLC, in his post, Cloud and Edge Computing — The Stats You Need to Know for 2018, estimates that the global edge computing market is expected to reach USD 6.72 billion by 2022 at a compound annual growth rate of a whopping 35.4 percent. Realizing the market potential, many major Cloud providers, edge device manufacturers, and integrators are rapidly expanding their edge compute capabilities. AWS, for example, currently offers more than a dozen services in their edge computing category.
Internet of Things
Edge computing is frequently associated with the Internet of Things (IoT). IoT devices, industrial equipment, and sensors generate data transmitted to other internal and external systems, often by way of edge nodes, such as an IoT Gateway. IoT devices typically generate time-series data. According to Wikipedia, a time series is a set of data points indexed in time order — a sequence taken at successive equally spaced points in time. IoT devices typically generate continuous high-volume streams of time-series data, often on a scale of millions of data points per second. IoT data characteristics require IoT platforms to minimally support temporal accuracy, high-volume ingestion and processing, efficient data compression and downsampling, and real-time querying capabilities.
Edge devices such as IoT Gateways, which aggregate and transmit IoT data from these devices to external systems, are generally lower-powered, with limited processors, memory, and storage. Accordingly, IoT platforms must satisfy all the requirements of IoT data while simultaneously supporting resource-constrained environments.
IoT Analytics at the Edge
Leading Cloud providers AWS, Azure, Google Cloud, IBM Cloud, Oracle Cloud, and Alibaba Cloud all offer IoT services. Many offer IoT services with edge computing capabilities. AWS offers AWS IoT Greengrass. Greengrass provides local compute, messaging, data management, sync, and machine learning (ML) inference capabilities to edge devices. Azure offers Azure IoT Edge. Azure IoT Edge provides the ability to run artificial intelligence (AI), Azure and third-party services, and custom business logic on edge devices using standard containers. Google Cloud offers Edge TPU. Edge TPU (Tensor Processing Unit) is Google’s purpose-built application-specific integrated circuit (ASIC), designed to run AI at the edge.
IoT Analytics
Many Cloud providers also offer IoT analytics as part of their suite of IoT services, although not at the edge. AWS offers AWS IoT Analytics, while Azure has Azure Time Series Insights. Google provides IoT analytics, indirectly, through downstream analytic systems and ad hoc analysis using Google BigQuery or advanced analytics and machine learning with Cloud Machine Learning Engine. These services generally all require data to be transmitted to the Cloud for analytics.
The ability to analyze real-time, streaming IoT data at the edge is critical to a rapid feedback loop. IoT edge analytics can accelerate anomaly detection and remediation, improve predictive maintenance capabilities, and expedite proactive inventory replenishment.
IoT Edge Analytics Stack
In my opinion, the ideal IoT edge analytics stack is comprised of lightweight, purpose-built, easily deployable and manageable, platform- and programming language-agnostic, open-source software components. The minimal IoT edge analytics stack should include:
- Lightweight message broker;
- Purpose-built time-series database;
- ANSI-standard SQL ad-hoc query engine;
- Data visualization tool;
- Simple deployment and management framework;
Each component should be purpose-built for IoT.
Lightweight Message Broker
We will use Eclipse Mosquitto as our message broker. According to the project’s description, Mosquitto is an open-source message broker that implements the Message Queuing Telemetry Transport (MQTT) protocol versions 5.0, 3.1.1, and 3.1. Mosquitto is lightweight and suitable for use on all devices, from low-power single-board computers (SBCs) to full-powered servers.
MQTT Client Library
We will interact with Mosquitto using Eclipse Paho. According to the project, the Eclipse Paho project provides open-source, mainly client-side implementations of MQTT and MQTT-SN in a variety of programming languages. MQTT and MQTT for Sensor Networks (MQTT-SN) are light-weight publish/subscribe messaging transports for TCP/IP and connectionless protocols, such as UDP, respectively.
We will be using Paho’s Python Client. The Paho Python Client provides a client class with support for both MQTT v3.1 and v3.1.1 on Python 2.7 or 3.x. The client also provides helper functions to make publishing messages to an MQTT server straightforward.
Time-Series Database
Time-series databases are optimal for storing IoT data. According to InfluxData, makers of a leading time-series database, InfluxDB, a time-series database (TSDB), is a database optimized for time-stamped or time-series data. Time series data are simply measurements or events that are tracked, monitored, downsampled, and aggregated over time. Jiao Xian of Alibaba Cloud has authored an insightful post on the time-series database ecosystem, What Are Time Series Databases? A few leading Cloud providers offer purpose-built time-series databases, though they are not available at the edge. AWS offers Amazon Timestream, and Alibaba Cloud offers Time Series Database.
InfluxDB is an excellent choice for a time-series database. It was my first choice, along with TimescaleDB, when developing this stack. However, InfluxDB Flux’s apparent incompatibilities with some ARM-based architecture ruled it out for inclusion in the stack for this particular post.
We will use TimescaleDB as our time-series database. TimescaleDB is the leading open-source relational database for time-series data. Described as ‘PostgreSQL for time-series,’ TimescaleDB is based on PostgreSQL, which provides full ANSI SQL, rock-solid reliability, and a massive ecosystem. TimescaleDB claims to achieve 10–100x faster queries than PostgreSQL, InfluxDB, and MongoDB, with native optimizations for time-series analytics.
TimescaleDB is designed for performing analytical queries, both through its native support for PostgreSQL’s full range of SQL functionality and additional functions native to TimescaleDB. These time-series optimized functions include Median/Percentile, Cumulative Sum, Moving Average, Increase, Rate, Delta, Time Bucket, Histogram, and Gap Filling.
Ad-hoc Data Query Engine
We have the option of using psql, the terminal-based front-end to PostgreSQL, to execute ad-hoc queries against TimescaleDB. The psql front-end enables you to enter queries interactively, issue them to PostgreSQL, and see the query results.
We also have the option of using pgAdmin, specifically the biarms/pgadmin4 Docker version, to execute ad-hoc queries and perform most other database tasks. pgAdmin is the most popular open-source administration and development platform for PostgreSQL. While several popular Docker versions of pgAdmin only support Linux AMD64 architectures, the biarms/pgadmin4 Docker version supports ARM-based devices.
Data Visualization
For data visualization, we will use Grafana. Grafana allows you to query, visualize, alert on, and understand metrics no matter where they are stored. With Grafana, you can create, explore, and share dashboards, fostering a data-driven culture. Grafana allows you to define thresholds visually and get notified via Slack, PagerDuty, and more. Grafana supports dozens of data sources, including MySQL, PostgreSQL, Elasticsearch, InfluxDB, TimescaleDB, Graphite, Prometheus, Google BigQuery, GraphQL, and Oracle. Grafana is extensible through a large collection of plugins.
Edge Deployment and Management Platform
Docker introduced the current industry standard for containers in 2013. Docker containers are a standardized unit of software that allows developers to isolate apps from their environment. We will use Docker to deploy the IoT edge analytics stack, referred to herein as the GTM Stack, composed of containerized versions of Grafana, TimescaleDB, Eclipse Mosquitto, and pgAdmin, to an ARMv7-based edge node. The acronym, GTM, comes from the three primary OSS projects composing the stack. The abbreviation also suggests Greenwich Mean Time, relating to the precise time-series nature of IoT data.
Running Docker Engine in swarm mode, we can use Docker to deploy the complete IoT edge analytics stack to the swarm, running on the edge node. The deploy command accepts a stack description in the form of a Docker Compose file, a YAML file used to configure the application’s services. With a single command, we can create and start all the services from the configuration file.
Source Code
All source code for this post is available on GitHub. Use the following command to git clone a local copy of the project. Note that the updated version of the source code for this post is in the v2021–03 branch.
git clone --branch v2021-03 --single-branch --depth 1 \
https://github.com/garystafford/iot-analytics-at-the-edge.git
IoT Devices
For this post, I have deployed three Linux ARM-based IoT devices, each connected to a sensor array. Each sensor array contains multiple analog and digital sensors. The sensors record temperature, humidity, air quality (liquefied petroleum gas (LPG), carbon monoxide (CO), and smoke), light, and motion. For more information on the IoT device and sensor hardware involved, please see my previous post.Getting Started with IoT Analytics on AWS
Analyze environmental sensor data from IoT devices in near real-time with AWS IoT Analyticstowardsdatascience.com
Each ARM-based IoT device runs a small Python3-based script, sensor_data_to_mosquitto.py, shown below.
The IoT devices’ script implements the Eclipse Paho MQTT Python client library. An MQTT message containing simultaneous readings from each sensor is sent to a Mosquitto topic on the edge node at a configurable frequency.
Below are the actual sensor readings sent by the IoT device as an MQTT message to the Mosquitto topic.
IoT Edge Node
For this post, I have deployed a single Linux ARM-based edge node. The three IoT devices containing sensor arrays communicate with the edge node over Wi-Fi. IoT devices could easily use an alternative communication protocol, such as BLE, LoRaWAN, or Ethernet. For more information on BLE and LoRaWAN, please see some of my previous posts:LoRa and LoRaWAN for IoT: Getting Started with LoRa and LoRaWAN Protocols for Low Power, Wide Area Networking of IoT and BLE and GATT for IoT: Getting Started with Bluetooth Low Energy (BLE) and Generic Attribute Profile (GATT) Specification for IoT.
The edge node also runs a small Python3-based script, mosquitto_to_timescaledb.py, shown below.
Like the IoT devices, the edge node’s script implements the Eclipse Paho MQTT Python client library. The script pulls MQTT messages off a Mosquitto topic(s), serializes the message payload to JSON, and writes the payload’s data to the TimescaleDB database. The edge node’s script accepts several arguments, which allow you to configure the necessary Mosquitto and TimescaleDB connection settings.
Why not use Telegraf?
Telegraf is a plugin-driven agent that collects, processes, aggregates, and writes metrics. There is a Telegraf output plugin, the PostgreSQL and TimescaleDB Output Plugin for Telegraf, produced by TimescaleDB. The plugin can replace the need to manage and maintain the above script. However, I chose not to use it because it is not yet an official Telegraf plugin. If the plugin was included in a Telegraf release, I would certainly encourage its use.
Script Management
Both Linux-based IoT devices and edge nodes run systemd system and service manager. To ensure the Python scripts keep running in the case of a system restart, we define a systemd unit. Units are objects that systemd knows how to manage. This is a standardized representation of system resources that can be managed by the suite of daemons and manipulated by the provided utilities. Each script has a systemd unit file. Below, we see the gtm_stack_mosquitto unit file, gtm_stack_mosquitto.service.
The gtm_stack_mosq_to_tmscl unit file, gtm_stack_mosq_to_tmscl.service, is nearly identical.
To install the gtm_stack_mosquitto.service systemd unit file on each IoT device, use the following commands:
Installing the gtm_stack_mosq_to_tmscl.service unit file on the edge node is nearly identical.
Docker Stack
The edge node runs the GTM Docker stack, stack.yml, in a swarm. As discussed earlier, the stack contains four containers: Eclipse Mosquitto, TimescaleDB, Grafana, and pgAdmin. The Mosquitto, TimescaleDB, and Grafana containers have paths within the containers bind-mounted to directories on the edge device. With bind-mounting, the container’s configuration and data will persist if the containers are removed and re-created. The containers are running on an isolated overlay network.
The GTM Docker stack is installed using the following commands on the edge node. We will assume Docker and git are pre-installed on the edge node for this post.
First, we will create several local directories on the edge device, which will be used to bind-mount to the Docker container’s directories. Below, we see the bind-mounted local directories with the eventual container’s contents stored within them.
Next, we copy the custom Mosquitto configuration file, mosquitto.conf, included in the project to the edge device’s correct location.
Lastly, we initialize the Docker swarm and deploy the stack.
docker service ls' command, showing the running GTM Stack containersTimescaleDB Setup
With the GTM stack running, we need to create a single Timescale hypertable, sensor_data, in the TimescaleDB demo_iot database to hold the incoming IoT sensor data. Hypertables, according to TimescaleDB, are designed to be easy to manage and to behave like standard PostgreSQL tables. Hypertables are comprised of many interlinked “chunk” tables. Commands made to the hypertable automatically propagate changes down to all of the chunks belonging to that hypertable.
I suggest using psql to execute the required DDL statements, which will create the hypertable and the proceeding views and database user permissions. All SQL statements are included in the project’s statements.sql file. One way to use psql is to install it on your local workstation, then use psql to connect to the remote edge node. I prefer to instantiate a local PostgreSQL Docker container instance running psql. I then use the local container’s psql client to connect to the edge node’s TimescaleDB database. For example, from my local machine, I run the following docker run command to connect to the edge node’s TimescaleDB database on the edge node, located locally at 192.168.1.12.
Although not as practical, you can also access psql from within the TimescaleDB Docker container, running on the actual edge node, using the following docker exec command.
TimescaleDB Continuous Aggregates
For this post’s demonstration, we will create four TimescaleDB materialized views, which will be queried from a Grafana Dashboard. The materialized views are TimescaleDB Continuous Aggregates. According to Timescale, aggregate queries which touch large swathes of time-series data can take a long time to compute because the system needs to scan large amounts of data on every query execution. To make these queries faster, a continuous aggregate allows materializing the computed aggregates, while also providing means to continuously, and with low overhead, keep them up-to-date as the underlying source data changes.
For example, we generate sensor data every five seconds from the three IoT devices in this post. When visualizing a 24-hour period in Grafana, using continuous aggregates with an interval of one minute, we would reduce the total volume of data queried from 51,840 rows to 4,320 rows, a reduction of over 91%. The larger the time period or the number of IoT devices being analyzed, the more significant these savings will positively impact query performance.
A time_bucket on the time partitioning column of the hypertable is required for all continuous aggregate views. The time_bucket function, in this case, has a bucket width (interval) of 1 minute. The interval is configurable.
To automatically refresh the four materialized views, we will create four corresponding continuous aggregate policies. In this demonstration, the continuous aggregate policies create a refresh window between one week ago and one hour ago, with a refresh interval of one hour.
Advanced Analytic Queries
The ability to perform ad-hoc queries on time-series IoT data is an essential feature of the IoT edge analytics stack. We can use psql, pgAdmin, or even our own IDE to perform ad-hoc queries against the TimescaleDB database on the edge node. Below are examples of typical ad-hoc queries a data analyst might perform on IoT sensor data. These example queries demonstrate TimescaleDB’s advanced analytical capabilities for working with time-series data, including Moving Average, Delta, Time Bucket, and Histogram.
Data Visualization with Grafana
Using the TimescaleDB continuous aggregates we have created, we can quickly build a richly featured dashboard in Grafana. Below we see a typical IoT Dashboard you might build to monitor the post’s IoT sensor data in near real-time. An exported version, dashboard_external_export.json, is included in the GitHub project.
Limiting Grafana’s Access to IoT Data
Following the Grafana recommendation for database user permissions, we create a grafanareader PostgresSQL user, and limit the user’s access to the sensor_data table and the four views we created. Grafana will use this user’s credentials to perform SELECT queries of the TimescaleDB demo_iot database.
Using PostgreSQL in Grafana
Grafana’s documentation includes a comprehensive set of instructions for Using PostgreSQL in Grafana. To connect to the TimescaleDB database from Grafana, we use the PostgreSQL data source plugin.
The data displayed in each Panel in the Grafana Dashboard is based on a SQL query. For example, the Average Temperature Panel might use a query similar to the example below. This particular query also converts Celsius to Fahrenheit. Note the use of Grafana Macros (e.g., $__time(), $__timeFilter()). Macros can be used within a query to simplify syntax and allow for dynamic parts.
SELECT
$__time(bucket),
device_id AS metric,
((avg_temp * 1.9) + 32) AS avg_temp
FROM temperature_humidity_summary_minute
WHERE
$__timeFilter(bucket)
ORDER BY 1,2
Below, we see another example from the Average Humidity Panel. In this particular query, we might choose to limit the humidity data to a valid range of 0%–100%.
SELECT
$__time(bucket),
device_id AS metric,
avg_humidity
FROM temperature_humidity_summary_minute
WHERE
$__timeFilter(bucket)
AND avg_humidity >= 0.0
AND avg_humidity <= 100.0
ORDER BY 1,2
Mobile Friendly
Grafana dashboards are mobile-friendly. Below we see two views of the dashboard, using the Chrome mobile browser on an Apple iPhone.
Grafana Alerts
Grafana allows Alerts to be created based on the Rules you define in each Panel. If data values match the Rule’s conditions, which you pre-define, such as a temperature reading above a certain threshold for a set amount of time, an alert is sent to your choice of destinations. According to the Rule shown below, If the average temperature exceeds 75°F for a period of 5 minutes, an alert is sent.
As demonstrated below, when the laboratory temperature began to exceed 75°F, the alert entered a ‘Pending’ state. If the temperature exceeded 75°F for the pre-determined period of 5 minutes, the alert status changes to ‘Alerting’, and an alert is sent. When the temperature dropped back below 75°F for the pre-determined period of 5 minutes, the alert status changed from ‘Alerting’ to ‘OK’, and a subsequent notification was sent.
There are currently twenty alert notifiers available out-of-the-box with Grafana, including Slack, email, PagerDuty, webhooks, VictorOps, Opsgenie, and Microsoft Teams. We can use Grafana Alerts to notify the proper resources, in near real-time, if an issue is detected based on the data. Below, we see an actual series of high-temperature alerts sent by Grafana to the Slack channel, followed by subsequent notifications as the temperature returned to normal.
Conclusion
This post explored the development of an IoT edge analytics stack comprised of lightweight, purpose-built, easily deployable and manageable platform- and programming language-agnostic, open-source software components. These components included Docker containerized versions of Grafana, TimescaleDB, Eclipse Mosquitto, and pgAdmin, referred to as the GTM Stack. Using the GTM stack, we collected, analyzed, and visualized IoT data without first shipping the data to Cloud or other external systems.
This blog represents my own viewpoints and not of my employer, Amazon Web Services (AWS). All product names, logos, and brands are the property of their respective owners.
AWS IoT Core for LoRaWAN, AWS IoT Analytics, and Amazon QuickSight
Posted by Gary A. Stafford in Analytics, AWS, Cloud, IoT, Python, Software Development on April 15, 2021
In the following post, we will learn how to monitor indoor air quality (IAQ) using a private LoRaWAN sensor device network. The devices transmit their sensor telemetry to AWS through a LoRaWAN gateway using the newly released AWS IoT Core for LoRaWAN service. We will then analyze and visualize the sensor data using AWS IoT Analytics and Amazon QuickSight.
Introduction
On December 15, 2020, AWS announced support for Semtech’s low-power, long-range wide area network (LoRaWAN) connectivity. LoRaWAN devices and gateways can now connect to AWS IoT Core using AWS IoT Core for LoRaWAN. AWS IoT Core for LoRaWAN is a fully managed feature that enables customers to connect wireless devices that use the LoRaWAN protocol with the AWS cloud. Using AWS IoT Core, customers can now set up a private LoRaWAN network by securely connecting their LoRaWAN devices and gateways to the AWS cloud — without developing or operating a LoRaWAN Network Server (LNS).
The LoRa Alliance describes the LoRaWAN specification as a Low Power, Wide Area (LPWA) networking protocol designed to wirelessly connect battery operated ‘things’ to the internet in regional, national, or global networks, and targets key Internet of Things (IoT) requirements such as bi-directional communication, end-to-end security, mobility and localization services. The LoRaWAN specification defines both the device-to-infrastructure (LoRa) physical layer parameters and the (LoRaWAN) protocol, providing seamless interoperability between manufacturers.
According to Wikipedia, LoRa (Long Range) is a proprietary low-power wide-area network modulation technique. It is based on spread spectrum modulation techniques derived from chirp spread spectrum (CSS) technology. It was developed by Cycleo of Grenoble, France, and acquired by Semtech, the founding member of the LoRa Alliance, and it is patented.
AWS-qualified Hardware
Along with the AWS IoT Core for LoRaWAN announcement, AWS released a list of qualified gateways found in the AWS Partner Device Catalog. The catalog helps customers discover qualified hardware that works with AWS services to help build and deliver successful IoT solutions. AWS IoT Core for LoRaWAN supports open-source gateway-LNS protocol software called LoRa Basics Station, an implementation of a LoRa packet forwarder. The AWS IoT Core for LoRaWAN gateway qualification program enables customers to source pre-tested LoRaWAN gateways and developer kits that meet the required LoRa Basics Station specifications.
LoRaWAN Gateway
In this post, we will use the AWS-qualified LoRaWAN-compliant MiniHub Pro gateway by Browan Communications. At $109, MiniHub Pro is a low-cost gateway that uses LoRa Basics Station to forward RF packets received from LoRaWAN devices (uplinks) to the LoRaWAN Network Server (LNS), part of the AWS IoT Core for LoRaWAN service. The MiniHub Pro is based on FreeRTOS, the real-time operating system for microcontrollers.
LoRaWAN Devices
In addition to the gateway, we will use a set of two Model TBHV110 915 Mhz Healthy Home Sensor IAQ (aka Tabs Healthy Home), also by Browan Communications Inc. According to Browan, the Healthy Home Sensor utilizes LoRaWAN connectivity (LoRaWAN 1.0.3) to communicate the temperature, relative humidity, volatile organic compound (VOC) levels, and indoor air quality (IAQ) of the surrounding environment. The Healthy Home Sensor costs $60 per sensor. The gateway and two devices used in this post were purchased from Cal-Chip Connected Devices at a total retail cost of $229 plus tax and shipping.
IAQ Index
The Healthy Home Sensor determines an IAQ Index, a reading between 0–500, indicating general air quality. According to the CDC, monitoring and maintaining good indoor air quality and proper ventilation have become essential to reduce the potential airborne spread of COVID-19.
Source Code
The source code for this post is available on GitHub. Use the following command to git clone a local copy of the project.
git clone --branch main --single-branch --depth 1 \
https://github.com/garystafford/lorawan-iot-core-demo.git
The Lambda function and its associated AWS resources are deployed using the AWS Serverless Application Model (SAM). The primary AWS resources used in this demonstration are shown in the architectural diagram below.
Adding Gateways and Devices
In combination with the manufacturer’s product manual, the AWS documentation walks you through adding your LoRaWAN gateways and devices with AWS IoT Core for LoRaWAN. Adding gateways and devices is easy and straightforward on AWS as long as you have the appropriate GatewayEUI, DevEUI, AppEUI (aka JoinEUI), and AppKey, supplied by the manufacturer or retailer.
Over-the-Air Activation (OTAA)
In this post, the gateway and sensor devices are connected to AWS IoT Core for LoRaWAN using Over-the-Air Activation (OTAA). According to both The Things Network and AWS, OTAA is preferred over Activation by Personalization (ABP) as it is more secure. When using OTAA, temporary session keys are derived from root keys on each activation. ABP uses static keys, is less secure, and should not be used if not explicitly required by the use case.
The LoRaWAN gateway is added to AWS IoT Core for LoRaWAN. The gateway is then configured locally using the information received from IoT Core. With the MiniHub Pro, you can enable FreeRTOS Over-the-Air Updates and Configuration and Update Server (CUPS).
Below, we see that the MiniHub Pro gateway has been successfully added to IoT Core and has exchanged uplink frames (RF packets) through that connection with the AWS IoT Core for LoRaWAN, which is acting as the LoRaWAN Network Server (LNS).
We then add the two Healthy Home Sensor devices in a similar fashion.
Below, we see that one of the two Healthy Home Sensor devices has successfully connected to AWS IoT Core for LoRaWAN and has also transmitted uplink frames via the MiniHub Pro gateway.
IoT Destinations
Messages from the devices are received from the gateway and routed to an IoT rule using a Destination. According to AWS, a destination describes the AWS IoT rule that processes the data from a wireless device so that AWS IoT services can use the data. Many devices can share a single destination.
IoT Rules
IoT rules, according to AWS, tell AWS IoT what to do when it receives messages from your devices. Rules extract data from messages, evaluate expressions using message data, and invoke one or more actions when the rule’s conditions are met.
Using AWS IoT Core for LoRaWAN, the device’s messages are base64 encoded binary messages. The IoT rule’s query statement is a SQL statement that determines which messages are forwarded to Actions. In the case of LoRaWAN, the query statement contains an inline call to an AWS Lambda function. The function accepts a number of input parameters. It decodes the base64 encoded message, decodes and translates the binary message, and builds a Python dictionary with the results. Finally, the dictionary is serialized to JSON and returned to the IoT rule.
Healthy Home Sensor Messages
LoRa uses a license-free sub-gigahertz radio frequency of 915 MHz in North America. The Healthy Home Sensor device transmits binary messages over this radio frequency using the LoRaWAN 1.0.3 specification. Binary messages sent by the Healthy Home Sensor device to the MiniHub Pro gateway have a payload length of 11 bytes, as follows:
The device encrypts its binary message, containing sensor data, using AES128 CTR mode before transmitting it. AWS IoT Core for LoRaWAN decrypts the binary message, then encodes the decrypted binary message payload as a base64 string.
The final JSON-format message delivered by AWS IoT Core for LoRaWAN the IoT rule contains the device’s base64 encoded binary message (PayloadData), along with additional information about the source LoRaWAN device and the LoRaWAN gateway that transmitted the message.
The IoT rule’s query statement calls a Lambda function to decode and transform the base64 encoded binary message. The Lambda calls the correct decoder, which contains logic to decode each byte of the binary message. AWS has developed the IoT Rule decoder Lambda along with several binary message payload decoders, freely available on GitHub, including:
- Browan Tabs Object Locator
- Dragino LHT65, LGT92, LSE01, LBT1, LDS01
- Axioma W1
- Elsys
- Globalsat LT-100
- NAS Pulse Reader UM3080
Since ASW did not include the Browan Healthy Home Sensor device in the list of provided decoders, I have created a new Python-based decoder using the message payload information detailed in the device’s product manual.
The decoder first decodes the base64 encoded binary message payload.
# base64 encoded binary message
AAs0LNsCAQB/ADM=
# base64 decoded 11-byte binary message
00000000 00001011 00110100 00101100 11011011 00000010 00000001 00000000 01111111 00000000 00110011
# as hexadecimal
00 0b 34 2c db 02 01 00 7f 00 33
# as decimal
0 11 52 44 219 2 1 0 127 0 51
The decoder then processes each byte of the binary message payload, bit-by-bit, and applies any additional logic to derive the final sensor values. Decoding the base64 encoded binary message payload and placing it back into the original message, we are left with the following message structure:
IoT Rule Actions
Messages returned by the rule’s query and processed by the Lambda function are passed to an AWS IoT rule action. AWS IoT rule actions specify what to do when a rule is triggered. This rule’s action sends a message to an AWS IoT Analytics channel.
AWS IoT Analytics
IoT Analytics, according to AWS, is a fully-managed service that makes it easy to run and operationalize sophisticated analytics on massive volumes of IoT data. IoT Analytics consists of five primary components: channels, pipelines, data stores, datasets, and notebooks.
The IoT rule sends messages to the IoT Analytics channel. The channel publishes the data to a pipeline. The pipeline consumes messages from the channel and enables you to process and filter the messages before storing them in the data store. The data store receives and stores the messages. You retrieve data from a data store by creating a SQL dataset or a container dataset. The SQL dataset contains a SQL query, which is executed against the data store. According to the documentation, both Amazon Athena and Amazon IoT Analytics’ SQL expressions are based on PrestoDB.
The SQL query I have written is specific to the data collected by the Healthy Home Sensor device. In Amazon QuickSight, we will use this dataset to construct an IAQ monitoring dashboard.
Amazon QuickSight
Amazon QuickSight, according to AWS, is a scalable, serverless, embeddable, machine learning-powered Business Intelligence (BI) service built for the cloud. QuickSight lets you easily create and publish interactive BI dashboards that include Machine Learning-powered insights. AWS IoT Analytics provides direct integration with Amazon QuickSight.
Using the ‘AWS IoT Analytics’ data source, we can build a QuickSight data set by importing the IoT Analytics dataset built in the previous step. In QuickSight, we are able to preview the data, change field types, apply filters, add calculated fields (e.g., sensor_location), and exclude fields if desired.
The final data is then saved (cached) in SPICE, QuickSight’s Super-fast, Parallel, In-memory Calculation Engine.https://programmaticponderings.wordpress.com/media/509f9c79155177fd1480eeee2bd23593Final data used for analysis
Using this dataset, we can build a QuickSight Analysis. The analysis will use a variety of visual types to display sensor data, such as temperature, relative humidity, volatile organic compound (VOC) levels, indoor air quality (IAQ), the sensor device’s battery levels, and the gateway’s SNR (Signal-to-Noise Ratio) and RSSI (Received Signal Strength Indication).
We can then securely share the Analysis we have built with data consumers as a QuickSight Dashboard, accessible through a web browser or using the Amazon QuickSight mobile app.
Below, we see how QuickSight is able to visualize indoor air quality data for multiple sensor locations over the course of a single day. In this visual, sensor data is captured and displayed in five-minute increments. The visual contains reference lines indicating both good and bad IAQ thresholds. Analyzing the results, we can quickly see how external and internal environmental conditions, HVAC systems, air filtration devices, external ventilation through windows and doors, and human activity all impact IAQ throughout the day in different areas of the dwelling.
Conclusion
In this post, we learned how easy it is to activate, configure, and start collecting sensor telemetry from LoRaWAN devices and gateways using the newly released AWS IoT Core for LoRaWAN service. We observed the seamless integration of AWS IoT Core, IoT Analytics, and Amazon QuickSight to transform, store, and visualize sensor data. Extending this example to add notifications and auto-remediation based on sensor data is equally as easy with services such as AWS IoT Events.
Reference
Employing Amazon Macie to Discover and Protect Sensitive Data in your Amazon S3-based Data Lake
Posted by Gary A. Stafford in AWS, Bash Scripting, Big Data, Cloud, Enterprise Software Development, Python on March 15, 2021
Introduction
Working with Analytics customers, it’s not uncommon to see data lakes with a dozen or more discrete data sources. Data typically originates from sources both internal and external to the customer. Internal data may come from multiple teams, departments, divisions, and enterprise systems. External data comes from vendors, partners, public sources, and subscriptions to licensed data sources. The volume, velocity, variety, veracity, and method of delivery vary across the data sources. All this data is being fed into data lakes for purposes such as analytics, business intelligence, and machine learning.
Given the growing volumes of incoming data and variations amongst data sources, it is increasingly complex, expensive, and time-consuming for organizations to ensure compliance with relevant laws, policies, and regulations. Regulations that impact how data is handled in a data lake include the Organizations Health Insurance Portability and Accountability Act (HIPAA), General Data Privacy Regulation (GDPR), Payment Card Industry Data Security Standard (PCI DSS), California Consumer Privacy Act (CCPA), and the Federal Information Security Management Act (FISMA).
Data Lake
AWS defines a data lake as a centralized repository that allows you to store all your structured and unstructured data at any scale. Once in the data lake, you run different types of analytics — from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.
Data in a data lake is regularly organized or separated by its stage in the analytics process. Incoming data is often referred to as raw data. Data is then processed — cleansed, filtered, enriched, and tokenized if necessary. Lastly, the data is analyzed and aggregated, and the results are written back to the data lake. The analyzed and aggregated data is used to build business intelligence dashboards and reports, machine learning models, and is delivered to downstream or external systems. The different categories of data — raw, processed, and aggregated, are frequently referred to as bronze, silver, and gold, a reference to their overall data quality or value.
Protecting the Data Lake
Imagine you’ve received a large volume of data from an external data source. The incoming data is cleansed, filtered, and enriched. The data is re-formatted, partitioned, compressed for analytical efficiency, and written back to the data lake. Your analytics pipelines run complex and time-consuming queries against the data. Unfortunately, while building reports for a set of stakeholders, you realize that the original data accidentally included credit card information and other sensitive information about your customers. In addition to being out of compliance, you have the wasted time and expense of the initial data processing, as well as the extra time and expense to replace and re-process the data. The solution — Amazon Macie.
Amazon Macie
According to AWS, Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect your sensitive data stored in Amazon Simple Storage Service (Amazon S3). Macie’s alerts, or findings, can be searched, filtered, and sent to Amazon EventBridge, formerly called Amazon CloudWatch Events, for easy integration with existing workflow or event management systems, or to be used in combination with AWS services, such as AWS Step Functions or Amazon Managed Workflows for Apache Airflow (MWAA) to take automated remediation actions.
Data Discovery and Protection
In this post, we will deploy an automated data inspection workflow to examine sample data in an S3-based data lake. Amazon Macie will examine data files uploaded to an encrypted S3 bucket. If sensitive data is discovered within the files, the files will be moved to an encrypted isolation bucket for further investigation. Email and SMS text alerts will be sent. This workflow will leverage Amazon EventBridge, Amazon Simple Notification Service (Amazon SNS), AWS Lambda, and AWS Systems Manager Parameter Store.
Source Code
Using this git clone command, download a copy of this post’s GitHub repository to your local environment.
git clone --branch main --single-branch --depth 1 --no-tags \
https://github.com/garystafford/macie-demo.git
AWS resources for this post can be deployed using AWS CloudFormation. To follow along, you will need recent versions of Python 3, Boto3, and the AWS CLI version 2, installed.
Sample Data
We will use synthetic patient data, freely available from the MITRE Corporation. The data was generated by Synthea, MITRE’s open-source, synthetic patient generator that models the medical history of synthetic patients. Synthea data is exported in a variety of data standards, including HL7 FHIR, C-CDA, and CSV. We will use CSV-format data files for this post. Download and unzip the CSV files from the Synthea website.
REMOTE_FILE="synthea_sample_data_csv_apr2020.zip"
wget "https://storage.googleapis.com/synthea-public/${REMOTE_FILE}"
unzip -j "${REMOTE_FILE}" -d synthea_data/
The sixteen CSV data files contain a total of 471,852 rows of data, including column headers.
> wc -l *.csv
598 allergies.csv
3,484 careplans.csv
8,377 conditions.csv
79 devices.csv
53,347 encounters.csv
856 imaging_studies.csv
15,479 immunizations.csv
42,990 medications.csv
299,698 observations.csv
1,120 organizations.csv
1,172 patients.csv
3,802 payer_transitions.csv
11 payers.csv
34,982 procedures.csv
5,856 providers.csv
1 supplies.csv
------------------------------
471,852 total
Amazon Macie Custom Data Identifier
To demonstrate some of the advanced features of Amazon Macie, we will use three Custom Data Identifiers. According to Macie’s documentation, a custom data identifier is a set of criteria that you define that reflects your organization’s particular proprietary data — for example, employee IDs, customer account numbers, or internal data classifications. We will create three custom data identifiers to detect the specific Synthea-format Patient ID, US driver number, and US passport number columns.
The custom data identifiers in this post use a combination of regular expressions (regex) and keywords. The identifiers are designed to work with structured data, such as CSV files. Macie reports text that matches the regex pattern if any of these keywords are in the name of the column or field that stores the text, or if the text is within the maximum match distance of one of these words in a field value. Macie supports a subset of the regex pattern syntax provided by the Perl Compatible Regular Expressions (PCRE) library.
Enable Macie
Before creating a CloudFormation stack with this demonstration’s resources, you will need to enable Amazon Macie from the AWS Management Console, or use the macie2 API and the AWS CLI with the enable-macie command.
aws macie2 enable-macie
Macie can also be enabled for your multi-account AWS Organization. The enable-organization-admin-account command designates an account as the delegated Amazon Macie administrator account for an AWS organization. For more information, see Managing multiple accounts in Amazon Macie.
AWS_ACCOUNT=111222333444
aws macie2 enable-organization-admin-account \
--admin-account-id ${AWS_ACCOUNT}
CloudFormation Stack
To create the CloudFormation stack with the supplied template, cloudformation/macie_demo.yml, run the following AWS CLI command. You will need to include an email address and phone number as input parameters. These parameter values will be used to send email and text alerts when Macie produces a sensitive data finding.
Please make sure you understand all the potential cost and security implications of creating the CloudFormation stack before continuing.
SNS_PHONE="+12223334444"
SNS_EMAIL="your-email-address@email.com"
aws cloudformation create-stack \
--stack-name macie-demo \
--template-body file://cloudformation/macie_demo.yml \
--parameters ParameterKey=SNSTopicEndpointSms,ParameterValue=${SNS_PHONE} \
ParameterKey=SNSTopicEndpointEmail,ParameterValue=${SNS_EMAIL} \
--capabilities CAPABILITY_NAMED_IAM
As shown in the AWS CloudFormation console, the new macie-demo stack will contain twenty-one AWS resources.
Upload Data
Next, with the stack deployed, upload the CSV format data files to the encrypted S3 bucket, representing your data lake. The target S3 bucket has the following naming convention, synthea-data-<aws_account_id>-<region>. You can retrieve the two new bucket names from AWS Systems Manager Parameter Store, which were written there by CloudFormation, using the ssm API.
aws ssm get-parameters-by-path \
--path /macie_demo/ \
--query 'Parameters[*].Value'
Use the following ssm and s3 API commands to upload the data files.
DATA_BUCKET=$(aws ssm get-parameter \
--name /macie_demo/patient_data_bucket \
--query 'Parameter.Value')
aws s3 cp synthea_data/ \
"s3://$(eval echo ${DATA_BUCKET})/patient_data/" --recursive
You should end up with sixteen CSV files in the S3 bucket, totaling approximately 82.3 MB.
Sensitive Data Discovery Jobs
With the CloudFormation stack created and the patient data files uploaded, we will create two sensitive data discovery jobs. These jobs will scan the contents of the encrypted S3 bucket for sensitive data and report the findings. According to the documentation, you can configure a sensitive data discovery job to run only once for on-demand analysis and assessment, or on a recurring basis for periodic analysis, assessment, and monitoring. For this demonstration, we will create a one-time sensitive data discovery job using the AWS CLI. We will also create a recurring sensitive data discovery job using the AWS SDK for Python (Boto3). Both jobs can also be created from within Macie’s Jobs console.
For both sensitive data discovery jobs, we will include the three custom data identifiers. Each of the custom data identifiers has a unique ID. We will need all three IDs to create the two sensitive data discovery jobs. You can use the AWS CLI and the macie2 API to retrieve the values.
aws macie2 list-custom-data-identifiers --query 'items[*].id'
Next, modify the job_specs/macie_job_specs_1x.json file, adding the three custom data identifier IDs. Also, update your AWS account ID and S3 bucket name (lines 3–5, 12, and 14). Note that since all the patient data files are in CSV format, we will limit our inspection to only files with a csv file extension (lines 18–33).
The above JSON template was generated using the standard AWS CLI generate-cli-skeleton command.
aws macie2 create-classification-job --generate-cli-skeleton
To create a one-time sensitive data discovery job using the above JSON template, run the following AWS CLI command. The unique job name will be dynamically generated based on the current time.
aws macie2 create-classification-job \
--name $(echo "SyntheaPatientData_${EPOCHSECONDS}") \
--cli-input-json file://job_specs/macie_job_specs_1x.json
In the Amazon Macie Jobs console, we can see a one-time sensitive data discovery job running. With a sampling depth of 100, the job will take several minutes to run. The samplingPercentage job property can be adjusted to scan any percentage of the data. If this value is less than 100, Macie selects the objects to analyze at random, up to the specified percentage and analyzes all the data in those objects.
Once the job is completed, the findings will be available in Macie’s Findings console. Using the three custom data identifiers in addition to Macie’s managed data identifiers, there should be a total of fifteen findings from the Synthea patient data files in S3. There should be six High severity findings and nine Medium severity findings. Of those, three are of a Personal finding type, seven of a Custom Identifier finding type, and five of a Multiple finding type, having both Personal and Custom Identifier finding types.
Isolating High Severity Findings
The data inspection workflow we have deployed uses an AWS Lambda function, macie-object-mover, to isolate all data files with High severity findings to a second S3 bucket. The offending files are copied to the isolation bucket and deleted from the source bucket.
Amazon EventBridge
According to Macie’s documentation, to support integration with other applications, services, and systems, such as monitoring or event management systems, Amazon Macie automatically publishes findings to Amazon EventBridge as finding events. Amazon EventBridge is a serverless event bus that makes it easier to build event-driven applications at scale using events generated from your applications, integrated Software-as-a-Service (SaaS) applications, and AWS services.
Each EventBridge rule contains an event pattern. The event pattern is used to filter the incoming stream of events for particular patterns. The EventBridge rule that is triggered when a Macie finding is based on any of the custom data identifiers, macie-rule-custom, uses the event pattern shown below. This pattern examines the finding event for the name of one of the three custom data identifier names that triggered it.
Each EventBridge rule contains an event pattern. The event pattern is used to filter the incoming stream of events for particular patterns. The EventBridge rule that is triggered when a Macie finding is based on one of the three custom data identifiers, macie-rule-high, uses the event pattern shown below. This pattern examines the finding event for the name of one of the three custom data identifier names that triggered it.
{
"source": [
"aws.macie"
],
"detail-type": [
"Macie Finding"
],
"detail": {
"classificationDetails": {
"result": {
"customDataIdentifiers": {
"detections": {
"name": [
"Patient ID",
"US Passport",
"US Driver License"
]
}
}
}
}
}
}
Six data files, containing High severity findings, will be moved to the isolation bucket by the Lambda, triggered by EventBridge.
Scheduled Sensitive Data Discovery Jobs
Data sources commonly deliver data on a repeated basis, such as nightly data feeds. For these types of data sources, we can schedule sensitive data discovery jobs to run on a scheduled basis. For this demonstration, we will create a scheduled job using the AWS SDK for Python (Boto3). Unlike the AWS CLI-based one-time job, you don’t need to modify the project’s script, scripts/create_macie_job_daily.py. The Python script will retrieve your AWS account ID and three custom data identifier IDs. The Python script then runs the create_classification_job command.
To create the scheduled sensitive data discovery job, run the following command.
python3 ./scripts/create_macie_job_daily.py
The scheduleFrequency parameter is set to { 'dailySchedule': {} }. This value specifies a daily recurrence pattern for running the job. The initialRun parameter of the create_classification_job command is set to True. This will cause the new job to analyze all eligible objects immediately after the job is created, in addition to on a daily basis.
Conclusion
In this post, we learned how we can use Amazon Macie to discover and protect sensitive data in Amazon S3. We learned how to use automation to trigger alerts based on Macie’s findings and to isolate data files based on the types of findings. The post’s data inspection workflow can easily be incorporated into existing data lake ingestion pipelines to ensure the integrity of incoming data.
This blog represents my own viewpoints and not of my employer, Amazon Web Services (AWS). All product names, logos, and brands are the property of their respective owners.
Running Spark Jobs on Amazon EMR with Apache Airflow: Using the new Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Service on AWS
Posted by Gary A. Stafford in AWS, Bash Scripting, Big Data, Build Automation, Cloud, DevOps, Python, Software Development on December 24, 2020
Introduction
In the first post of this series, we explored several ways to run PySpark applications on Amazon EMR using AWS services, including AWS CloudFormation, AWS Step Functions, and the AWS SDK for Python. This second post in the series will examine running Spark jobs on Amazon EMR using the recently announced Amazon Managed Workflows for Apache Airflow (Amazon MWAA) service.
Amazon EMR
According to AWS, Amazon Elastic MapReduce (Amazon EMR) is a Cloud-based big data platform for processing vast amounts of data using common open-source tools such as Apache Spark, Hive, HBase, Flink, Hudi, and Zeppelin, Jupyter, and Presto. Using Amazon EMR, data analysts, engineers, and scientists are free to explore, process, and visualize data. EMR takes care of provisioning, configuring, and tuning the underlying compute clusters, allowing you to focus on running analytics.
Users interact with EMR in a variety of ways, depending on their specific requirements. For example, you might create a transient EMR cluster, execute a series of data analytics jobs using Spark, Hive, or Presto, and immediately terminate the cluster upon job completion. You only pay for the time the cluster is up and running. Alternatively, for time-critical workloads or continuously high volumes of jobs, you could choose to create one or more persistent, highly available EMR clusters. These clusters automatically scale compute resources horizontally, including the use of EC2 Spot instances, to meet processing demands, maximizing performance and cost-efficiency.
AWS currently offers 5.x and 6.x versions of Amazon EMR. Each major and minor release of Amazon EMR offers incremental versions of nearly 25 different, popular open-source big-data applications to choose from, which Amazon EMR will install and configure when the cluster is created. The latest Amazon EMR releases are Amazon EMR Release 6.2.0 and Amazon EMR Release 5.32.0.
Amazon MWAA
Apache Airflow is a popular open-source platform designed to schedule and monitor workflows. According to Wikipedia, Airflow was created at Airbnb in 2014 to manage the company’s increasingly complex workflows. From the beginning, the project was made open source, becoming an Apache Incubator project in 2016 and a top-level Apache Software Foundation project (TLP) in 2019.
Many organizations build, manage, and maintain Apache Airflow on AWS using compute services such as Amazon EC2 or Amazon EKS. Amazon recently announced Amazon Managed Workflows for Apache Airflow (Amazon MWAA). With the announcement of Amazon MWAA in November 2020, AWS customers can now focus on developing workflow automation, while leaving the management of Airflow to AWS. Amazon MWAA can be used as an alternative to AWS Step Functions for workflow automation on AWS.
Apache recently announced the release of Airflow 2.0.0 on December 17, 2020. The latest 1.x version of Airflow is 1.10.14, released December 12, 2020. However, at the time of this post, Amazon MWAA was running Airflow 1.10.12, released August 25, 2020. Ensure that when you are developing workflows for Amazon MWAA, you are using the correct Apache Airflow 1.10.12 documentation.
The Amazon MWAA service is available using the AWS Management Console, as well as the Amazon MWAA API using the latest versions of the AWS SDK and AWS CLI.
Airflow has a mechanism that allows you to expand its functionality and integrate with other systems. Given its integration capabilities, Airflow has extensive support for AWS, including Amazon EMR, Amazon S3, AWS Batch, Amazon RedShift, Amazon DynamoDB, AWS Lambda, Amazon Kinesis, and Amazon SageMaker. Outside of support for Amazon S3, most AWS integrations can be found in the Hooks, Secrets, Sensors, and Operators of Airflow codebase’s contrib section.
Getting Started
Source Code
Using this git clone command, download a copy of this post’s GitHub repository to your local environment.
git clone --branch main --single-branch --depth 1 --no-tags \
https://github.com/garystafford/aws-airflow-demo.git
Preliminary Steps
This post assumes the reader has completed the demonstration in the previous post, Running PySpark Applications on Amazon EMR Methods for Interacting with PySpark on Amazon Elastic MapReduce. This post will re-use many of the last post’s AWS resources, including the EMR VPC, Subnets, Security Groups, AWS Glue Data Catalog, Amazon S3 buckets, EMR Roles, EC2 key pair, AWS Systems Manager Parameter Store parameters, PySpark applications, and Kaggle datasets.
Configuring Amazon MWAA
The easiest way to create a new MWAA Environment is through the AWS Management Console. I strongly suggest that you review the pricing for Amazon MWAA before continuing. The service can be quite costly to operate, even when idle, with the smallest Environment class potentially running into the hundreds of dollars per month.
Using the Console, create a new Amazon MWAA Environment. The Amazon MWAA interface will walk you through the creation process. Note the current ‘Airflow version’, 1.10.12.
Amazon MWAA requires an Amazon S3 bucket to store Airflow assets. Create a new Amazon S3 bucket. According to the documentation, the bucket must start with the prefix airflow-. You must also enable Bucket Versioning on the bucket. Specify a dags folder within the bucket to store Airflow’s Directed Acyclic Graphs (DAG). You can leave the next two options blank since we have no additional Airflow plugins or additional Python packages to install.
With Amazon MWAA, your data is secure by default as workloads run within their own Amazon Virtual Private Cloud (Amazon VPC). As part of the MWAA Environment creation process, you are given the option to have AWS create an MWAA VPC CloudFormation stack.
For this demonstration, choose to have MWAA create a new VPC and associated networking resources.
The MWAA CloudFormation stack contains approximately 22 AWS resources, including a VPC, a pair of public and private subnets, route tables, an Internet Gateway, two NAT Gateways, and associated Elastic IPs (EIP). See the MWAA documentation for more details.
As part of the Amazon MWAA Networking configuration, you must decide if you want web access to Airflow to be public or private. The details of the network configuration can be found in the MWAA documentation. I am choosing public webserver access for this demonstration, but the recommended choice is private for greater security. With the public option, AWS still requires IAM authentication to sign in to the AWS Management Console in order to access the Airflow UI.
You must select an existing VPC Security Group or have MWAA create a new one. For this demonstration, choose to have MWAA create a Security Group for you.
Lastly, select an appropriately-sized Environment class for Airflow based on the scale of your needs. The mw1.small class will be sufficient for this demonstration.
Finally, for Permissions, you must select an existing Airflow execution service role or create a new role. For this demonstration, create a new Airflow service role. We will later add additional permissions.
Airflow Execution Role
As part of this demonstration, we will be using Airflow to run Spark jobs on EMR (EMR Steps). To allow Airflow to interact with EMR, we must increase the new Airflow execution role’s default permissions. Additional permissions include allowing the new Airflow role to assume the EMR roles using iam:PassRole. For this demonstration, we will include the two default EMR Service and JobFlow roles, EMR_DefaultRole and EMR_EC2_DefaultRole. We will also include the corresponding custom EMR roles created in the previous post, EMR_DemoRole and EMR_EC2_DemoRole. For this demonstration, the Airflow service role also requires three specific EMR permissions as shown below. Later in the post, Airflow will also read files from S3, which requires s3:GetObject permission.
Create a new policy by importing the project’s JSON file, iam_policy/airflow_emr_policy.json, and attach the new policy to the Airflow service role. Be sure to update the AWS Account ID in the file with your own Account ID.
The Airflow service role, created by MWAA, is shown below with the new policy attached.
Final Architecture
Below is the final high-level architecture for the post’s demonstration. The diagram shows the approximate route of a DAG Run request, in red. The diagram includes an optional S3 Gateway VPC endpoint, not detailed in the post, but recommended for additional security. According to AWS, a VPC endpoint enables you to privately connect your VPC to supported AWS services and VPC endpoint services powered by AWS PrivateLink without requiring an internet gateway. In this case a private connection between the MWAA VPC and Amazon S3. It is also possible to create an EMR Interface VPC Endpoint to securely route traffic directly to EMR from MWAA, instead of connecting over the Internet.
Amazon MWAA Environment
The new MWAA Environment will include a link to the Airflow UI.
Airflow UI
Using the supplied link, you should be able to access the Airflow UI using your web browser.
Our First DAG
The Amazon MWAA documentation includes an example DAG, which contains one of several sample programs, SparkPi, which comes with Spark. I have created a similar DAG that is included in the GitHub project, dags/emr_steps_demo.py. The DAG will create a minimally-sized single-node EMR cluster with no Core or Task nodes. The DAG will then use that cluster to submit the calculate_pi job to Spark. Once the job is complete, the DAG will terminate the EMR cluster.
Upload the DAG to the Airflow S3 bucket’s dags directory. Substitute your Airflow S3 bucket name in the AWS CLI command below, then run it from the project’s root.
aws s3 cp dags/spark_pi_example.py \
s3://<your_airflow_bucket_name>/dags/
The DAG, spark_pi_example, should automatically appear in the Airflow UI. Click on ‘Trigger DAG’ to create a new EMR cluster and start the Spark job.
The DAG has no optional configuration to input as JSON. Select ‘Trigger’ to submit the job, as shown below.
The DAG should complete all three tasks successfully, as shown in the DAG’s ‘Graph View’ tab below.
Switching to the EMR Console, you should see the single-node EMR cluster being created.
On the ‘Steps’ tab, you should see that the ‘calculate_pi’ Spark job has been submitted and is waiting for the cluster to be ready to be run.
Triggering DAGs Programmatically
The Amazon MWAA service is available using the AWS Management Console, as well as the Amazon MWAA API using the latest versions of the AWS SDK and AWS CLI. To automate the DAG Run, we could use the AWS CLI and invoke the Airflow CLI via an endpoint on the Apache Airflow Webserver. The Amazon MWAA documentation and Airflow’s CLI documentation explains how.
Below is an example of triggering the spark_pi_example DAG programmatically using Airflow’s trigger_dag CLI command. You will need to replace the WEB_SERVER_HOSTNAME variable with your own Airflow Web Server’s hostname. The ENVIROMENT_NAME variable assumes only one MWAA environment is returned by jq.
Analytics Job with Airflow
Next, we will submit an actual analytics job to EMR. If you recall from the previous post, we had four different analytics PySpark applications, which performed analyses on the three Kaggle datasets. For the next DAG, we will run a Spark job that executes the bakery_sales_ssm.py PySpark application. This job should already exist in the processed data S3 bucket.
The DAG, dags/bakery_sales.py, creates an EMR cluster identical to the EMR cluster created with the run_job_flow.py Python script in the previous post. All EMR configuration options available when using AWS Step Functions are available with Airflow’s airflow.contrib.operators and airflow.contrib.sensors packages for EMR.
Airflow leverages Jinja Templating and provides the pipeline author with a set of built-in parameters and macros. The Bakery Sales DAG contains eleven Jinja template variables. Seven variables will be configured in the Airflow UI by importing a JSON file into the ‘Admin’ ⇨ ‘Variables’ tab. These template variables are prefixed with var.value in the DAG. The other three variables will be passed as a DAG Run configuration as a JSON blob, similar to the previous DAG example. These template variables are prefixed with dag_run.conf.
Import Variables into Airflow UI
First, to import the required variables, change the values in the project’s airflow_variables/admin_variables_bakery.json file. You will need to update the values for bootstrap_bucket, emr_ec2_key_pair, logs_bucket, and work_bucket. The three S3 buckets should all exist from the previous post.
Next, import the variables file from the ‘Admin’ ⇨ ‘Variables’ tab of the Airflow UI.
Upload the DAG, dags/bakery_sales.py, to the Airflow S3 bucket, similar to the first DAG.
aws s3 cp dags/bakery_sales.py \
s3://<your_airflow_bucket_name>/dags/
The second DAG, bakery_sales, should automatically appear in the Airflow UI. Click on ‘Trigger DAG’ to create a new EMR cluster and start the Spark job.
Input the three required parameters in the ‘Trigger DAG’ interface, used to pass the DAG Run configuration, and select ‘Trigger’. A sample of the JSON blob can be found in the project, airflow_variables/dag_run.conf_bakery.json.
{
"airflow_email": "analytics_team@example.com",
"email_on_failure": false,
"email_on_retry": false
}
This is just for demonstration purposes. To send and receive emails, you will need to configure Airflow.
Switching to the EMR Console, you should see the ‘Bakery Sales’ Spark job in the ‘Steps’ tab.
Multi-Step DAG
In our last example, we will use a single DAG to run four Spark jobs in parallel. The Spark job arguments (EmrAddStepsOperator steps parameter) will be loaded from an external JSON file residing in Amazon S3, instead of defined in the DAG, as in the previous two DAG examples. Additionally, the EMR cluster specifications (EmrCreateJobFlowOperator job_flow_overrides parameter) will also be loaded from an external JSON file. Using this method, we decouple the EMR provisioning and job details from the DAG. DataOps or DevOps Engineers might manage the EMR cluster specifications as code, while Data Analysts manage the Spark job arguments, separately. A third team might manage the DAG itself.
We still maintain the variables in the JSON files. The DAG will read the JSON file-based configuration into the tasks as JSON blobs, then replace the Jinja template variables (expressions) in the DAG with variable values defined in Airflow or input as parameters when the DAG is triggered.
Below we see a snippet of two of the four Spark submit-job job definitions (steps), which have been moved to a separate JSON file, emr_steps/emr_steps.json.
Below are the EMR cluster specifications (job_flow_overrides), which have been moved to a separate JSON file, job_flow_overrides/job_flow_overrides.json.
Decoupling the configurations reduces the DAG from well over 200 lines of code to less than 75 lines. Note lines 56 and 63 of the DAG below. Instead of referencing a local object variable, the parameters now reference the function, get_objects(key, bucket_name), which loads the JSON.
This time, we need to upload three files to S3, the DAG to the Airflow S3 bucket, and the two JSON files to the EMR Work S3 bucket. Change the bucket names to match your environment, then run the three AWS CLI commands shown below.
aws s3 cp emr_steps/emr_steps.json \
s3://emr-demo-work-123412341234-us-east-1/emr_steps/
aws s3 cp job_flow_overrides/job_flow_overrides.json \
s3://emr-demo-work-123412341234-us-east-1/job_flow_overrides/
aws s3 cp dags/multiple_steps.py \
s3://airflow-123412341234-us-east-1/dags/
The second DAG, multiple_steps, should automatically appear in the Airflow UI. Click on ‘Trigger DAG’ to create a new EMR cluster and start the Spark job. The three required input parameters in the ‘Trigger DAG’ interface are identical to the previous bakery_sales DAG. A sample of that JSON blob can be found in the project at airflow_variables/dag_run.conf_bakery.json.
Below we see that the EMR cluster has completed the four Spark jobs (EMR Steps) and has auto-terminated. Note that all four jobs were started at the exact same time. If you recall from the previous post, this is possible because we preset the ‘Concurrency’ level to 5.
Triggering DAGs Programmatically
AWS CLI
Similar to the previous example, below we can trigger the multiple_steps DAG programmatically using Airflow’s trigger_dag CLI command. Note the addition of the —-conf named argument, which passes the configuration, containing three key/value pairs, to the trigger command as a JSON blob.
AWS SDK
Airflow DAGs can also be triggered using the AWS SDK. For example, with boto3 for Python, we could use a script, similar to the following to remotely trigger a DAG.
Cleaning Up
Once you are done with the MWAA Environment, be sure to delete it as soon as possible to save additional costs. Also, delete the MWAA-VPC CloudFormation stack. These resources, like the two NAT Gateways, will also continue to generate additional costs.
aws mwaa delete-environment --name <your_mwaa_environment_name>
aws cloudformation delete-stack --stack-name MWAA-VPC
Conclusion
In this second post in the series, we explored using the newly released Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run PySpark applications on Amazon Elastic MapReduce (Amazon EMR). In future posts, we will explore the use of Jupyter and Zeppelin notebooks for data science, scientific computing, and machine learning on EMR.
If you are interested in learning more about configuring Amazon MWAA and Airflow, see my recent post, Amazon Managed Workflows for Apache Airflow — Configuration: Understanding Amazon MWAA’s Configuration Options.
This blog represents my own viewpoints and not of my employer, Amazon Web Services. All product names, logos, and brands are the property of their respective owners.
Installing Apache Superset on Amazon EMR: Add data exploration and visualization to your analytics cluster
Posted by Gary A. Stafford in AWS, Bash Scripting, Big Data, Build Automation, Cloud, Python on December 24, 2020
Introduction
AWS provides nearly twenty-five different open-source data analytics applications that can be automatically installed and configured on Amazon Elastic MapReduce (Amazon EMR). Of all those options, EMR doesn’t offer a general-purpose data exploration and visualization tool. However, with EMR, you can automate the installation of additional software as part of the cluster creation process or post cluster creation. This brief post will explore how to install, configure, and access Apache Superset, the modern data exploration and visualization platform on Amazon EMR’s Master Node, as a post-cluster creation step. You can use these same techniques to install other software packages on EMR as well, manually or as part of an automated Data Pipeline.
Amazon EMR
According to AWS, Amazon EMR is a cloud-based big data platform for processing vast amounts of data using open source tools such as Apache Spark, Hive, HBase, Flink, Hudi, and Zeppelin, Jupyter, and Presto. Using Amazon EMR, data analysts, engineers, and scientists are free to explore, process, and visualize data. EMR takes care of provisioning, configuring, and tuning the underlying compute clusters, allowing you to focus on running analytics.
AWS currently offers 5.x and 6.x versions of Amazon EMR. The latest Amazon EMR releases are Amazon EMR Release 6.2.0 and Amazon EMR Release 5.32.0. Each version of Amazon EMR offers incremental major and minor releases of nearly 25 different, popular open-source big-data software packages to choose from, which Amazon EMR will install and configure when the cluster is created.
Apache Superset
According to its website, Apache Superset is a modern data exploration and visualization platform. Superset is fast, lightweight, intuitive, and loaded with options that make it easy for users of all skill sets to explore and visualize their data, from simple line charts to highly detailed geospatial charts.
Superset natively supports over twenty-five data sources, including Amazon Athena and Redshift, Apache Drill, Druid, Hive, Impala, Kylin, Pinot, and Spark SQL, Elasticsearch, Google BigQuery, Hana, MySQL, Oracle, Postgres, Presto, Snowflake, Microsoft SQL Server, and Teradata.
As shown in their Gallery, Superset includes dozens of visualization types, including Pivot Table, Line Chart, Markup, Pie Chart, Filter Box, Bubble Chart, Box Plot, Histogram, Heatmap, Sunburst, Calendar Heatmap, and several geospatial types.
Setup
Using this git clone command, download a copy of this post’s open-source GitHub repository to your local environment.
git clone --branch main --single-branch --depth 1 --no-tags \
https://github.com/garystafford/emr-superset-demo.git
To demonstrate how to install Apache Superset on EMR, I have prepared an AWS CloudFormation template. Deploying the template, cloudformation/superset-emr-demo.yml, to AWS will result in the AWS CloudFormation stack, superset-emr-demo-dev. The stack creates a minimally-sized, two-node EMR cluster, two Amazon S3 buckets, and several AWS Systems Manager (SSM) Parameter Store parameters.
There is also a JSON-format CloudFormation parameters file, cloudformation/superset-emr-demo-params-dev.json. The parameters file contains values for eight of the ten required parameters in the CloudFormation template, all of which you can adjust. For the remaining two required parameters, you will need to supply the name of an existing EC2 key pair to access the EMR Master node. The key pair will need to be deployed to the same AWS Account into which you are deploying EMR. You will also need to supply a Subnet ID for the EMR cluster to be installed into. The subnet must have access to the Internet to install Superset’s required system and Python packages and to access Superset’s web-based user interface. If you need help creating a VPC and subnet to deploy EMR into, refer to my previous blog post, Running PySpark Applications on Amazon EMR: Methods for Interacting with PySpark on Amazon Elastic MapReduce.
The CloudFormation stack is created using a Python script, create_cfn_stack.py. The python script uses the AWS boto3 Python SDK.
To execute the Python script and create the CloudFormation stack, which will create the EMR cluster, run the following command. Remember to update the parameters to the name of your EC2 key pair and the Subnet ID for the EMR cluster.
python3 ./create_cfn_stack.py \
--ec2-key-name <your_key_pair_name> \
--ec2-subnet-id <your_subnet_id> \
--environment dev
Here is what the complete CloudFormation workflow looks like.
Security Group Ingress Rules
To install Superset on the EMR cluster’s Master node via SSH, you need to open port 22 on the Security Group associated with the EMR cluster’s Master Node, allowing access from your IP address. You can use the AWS Management Console or AWS CLI to open port 22. We will use jq and AWS ec2 API from the AWS CLI to get the Security Group ID associated with the EMR cluster’s Master Node and create the two ingress rules.
export EMR_MASTER_SG_ID=$(aws ec2 describe-security-groups | \
jq -r ".SecurityGroups[] | \
select(.GroupName==\"ElasticMapReduce-master\").GroupId" | \
head -n 1)
aws ec2 authorize-security-group-ingress \
--group-id ${EMR_MASTER_SG_ID} \
--protocol tcp \
--port 22 \
--cidr $(curl ipinfo.io/ip)/32
Superset Script
Once the CloudFormation stack is created and the ports are open, we can install Apache Superset on the EMR Master Node. The bootstrap script,bootstrap_emr/bootstrap_superset.sh, will be used to install Apache Superset onto the EMR cluster’s Master Node as the hadoop user. The script is roughly based on Superset’s Installing from Scratch instructions.
As part of installing Superset, the script will also deploy several common database drivers, including Amazon Athena, Amazon Redshift, Apache Spark SQL, Presto, PostgreSQL, and MySQL. The script will also create a Superset Admin role, and two Superset User roles — Alpha and Gamma.
To install Superset using the bootstrap script, we will use another Python script, install_superset.py. The script uses paramiko, a Python implementation of SSHv2. The script also uses scp, a module that uses a paramiko transport to send and receive files via the scp1 protocol.
The script requires a single input parameter, ec2-key-path, which is the full path to your EC2 key pair (e.g., ~/.ssh/my-key-pair.pem). Optionally, you can change the default Superset port of 8280, using the superset-port parameter.
python3 ./install_superset.py \
--ec2-key-path </path/to/my-key-pair.pem> \
--superset-port 8280
The script uses SSH and SCP to deploy and execute the bootstrap script,bootstrap_superset.sh. The output from the script includes the URL of Apache Superset running on the EMR cluster. The output also contains the username and password of the Superset Admin.
******************************************************************** Superset URL: http://ec2-111-22-333-44.compute-1.amazonaws.com:8280 Admin Username: SupersetAdmin Admin Password: Admin1234 ********************************************************************
SSH Tunnel
According to AWS, EMR applications publish user interfaces as websites hosted on the master node. For security reasons, these websites are only available on the master node’s local web server. To reach any of the web interfaces, you must establish an SSH tunnel with the master node using either dynamic or local port forwarding. If you are using dynamic port forwarding, you must also configure a proxy server to view the web interfaces.
Running the command in your terminal will start the SSH tunnel on port 8157. Once the tunnel is enabled, you can access Apache Superset in a web browser, using the script output’s URL shown in the script output above. Use the Admin credentials or either of the two User credentials to sign in to Superset.
Once signed in, you will have the ability to connect to your data sources and explore and visualize data. Below, we see an example of a SQL query executed against an Amazon RDS for PostgreSQL database, running in a separate VPC from EMR.
Conclusion
In this post, we learned how to install Apache Superset onto the Master Node of an Amazon EMR Cluster. If you want to install an application on all the nodes of an EMR cluster, you can add the commands to the bootstrap script, which runs when CloudFormation creates the cluster.
This blog represents my own viewpoints and not of my employer, Amazon Web Services. All product names, logos, and brands are the property of their respective owners.
Running PySpark Applications on Amazon EMR: Methods for Interacting with PySpark on Amazon Elastic MapReduce
Posted by Gary A. Stafford in AWS, Build Automation, Cloud, Python, Software Development on December 2, 2020
Introduction
According to AWS, Amazon Elastic MapReduce (Amazon EMR) is a Cloud-based big data platform for processing vast amounts of data using common open-source tools such as Apache Spark, Hive, HBase, Flink, Hudi, and Zeppelin, Jupyter, and Presto. Using Amazon EMR, data analysts, engineers, and scientists are free to explore, process, and visualize data. EMR takes care of provisioning, configuring, and tuning the underlying compute clusters, allowing you to focus on running analytics.
Users interact with EMR in a variety of ways, depending on their specific requirements. For example, you might create a transient EMR cluster, execute a series of data analytics jobs using Spark, Hive, or Presto, and immediately terminate the cluster upon job completion. You only pay for the time the cluster is up and running. Alternatively, for time-critical workloads or continuously high volumes of jobs, you could choose to create one or more persistent, highly available EMR clusters. These clusters automatically scale compute resources horizontally, including EC2 Spot instances, to meet processing demands, maximizing performance and cost-efficiency.
With EMR, individuals and teams can also use notebooks, including EMR Notebooks, based on JupyterLab, the web-based interactive development environment for Jupyter notebooks for ad-hoc data analytics. Apache Zeppelin is also available to collaborate and interactively explore, process, and visualize data. With EMR notebooks and the EMR API, users can programmatically execute a notebook without the need to interact with the EMR console, referred to as headless execution.
AWS currently offers 5.x and 6.x versions of Amazon EMR. Each major and minor release of Amazon EMR offers incremental versions of nearly 25 different, popular open-source big-data applications to choose from, which Amazon EMR will install and configure when the cluster is created. One major difference between EMR versions relevant to this post is EMR 6.x’s support for the latest Hadoop and Spark 3.x frameworks. The latest Amazon EMR releases are Amazon EMR Release 6.2.0 and Amazon EMR Release 5.32.0.
PySpark on EMR
In the following series of posts, we will focus on the options available to interact with Amazon EMR using the Python API for Apache Spark, known as PySpark. We will divide the methods for accessing PySpark on EMR into two categories: PySpark applications and notebooks. We will explore both interactive and automated patterns for running PySpark applications (Python scripts) and PySpark-based notebooks. In this first post, I will cover the first four PySpark Application Methods listed below. In part two, I will cover Amazon Managed Workflows for Apache Airflow (Amazon MWAA), and in part three, the use of notebooks.
PySpark Application Methods
- Add Job Flow Steps: Remote execution of EMR Steps on an existing EMR cluster using the
add_job_flow_stepsmethod; - EMR Master Node: Remote execution over SSH of PySpark applications using
spark-submiton an existing EMR cluster’s Master node; - Run Job Flow: Remote execution of EMR Steps on a newly created long-lived or auto-terminating EMR cluster using the
run_job_flowmethod; - AWS Step Functions: Remote execution of EMR Steps using AWS Step Functions on an existing or newly created long-lived or auto-terminating EMR cluster;
- Apache Airflow: Remote execution of EMR Steps using the recently released Amazon MWAA on an existing or newly created long-lived or auto-terminating EMR cluster (see part two of this series);
Notebook Methods
- EMR Notebooks for Ad-hoc Analytics: Interactive, ad-hoc analytics and machine learning using Jupyter Notebooks on an existing EMR cluster;
- Headless Execution of EMR Notebooks: Headless execution of notebooks from an existing EMR cluster or newly created auto-terminating cluster;
- Apache Zeppelin for Ad-hoc Analytics: Interactive, ad-hoc analytics and machine learning using Zeppelin notebooks on an existing EMR cluster;
Note that wherever the AWS SDK for Python (boto3) is used in this post, we can substitute the AWS CLI or AWS Tools for PowerShell. Typically, these commands and Python scripts would be run as part of a DevOps or DataOps deployment workflow, using CI/CD platforms like AWS CodePipeline, Jenkins, Harness, CircleCI, Travis CI, or Spinnaker.
Preliminary Tasks
To prepare the AWS EMR environment for this post, we need to perform a few preliminary tasks.
- Download a copy of this post’s GitHub repository;
- Download three Kaggle datasets and organize locally;
- Create an Amazon EC2 key pair;
- Upload the EMR bootstrap script and create the CloudFormation Stack;
- Allow your IP address access to the EMR Master node on port 22;
- Upload CSV data files and PySpark applications to S3;
- Crawl the raw data and create a Data Catalog using AWS Glue;
Step 1: GitHub Repository
Using this git clone command, download a copy of this post’s GitHub repository to your local environment.
git clone --branch main --single-branch --depth 1 --no-tags \
https://github.com/garystafford/emr-demo.git
Step 2: Kaggle Datasets
Kaggle is a well-known data science resource with 50,000 public datasets and 400,000 public notebooks. We will be using three Kaggle datasets in this post. You will need to join Kaggle to access these free datasets. Download the following three Kaggle datasets as CSV files. Since we are working with (moderately) big data, the total size of the datasets will be approximately 1 GB.
- Movie Ratings: https://www.kaggle.com/rounakbanik/the-movies-dataset
- Bakery: https://www.kaggle.com/sulmansarwar/transactions-from-a-bakery
- Stocks: https://www.kaggle.com/timoboz/stock-data-dow-jones
Organize the (38) downloaded CSV files into the raw_data directory of the locally cloned GitHub repository, exactly as shown below. We will upload these files to Amazon S3, in the proceeding step.
> tree raw_data --si -v -A
raw_data
├── [ 128] bakery
│ ├── [711k] BreadBasket_DMS.csv
├── [ 320] movie_ratings
│ ├── [190M] credits.csv
│ ├── [6.2M] keywords.csv
│ ├── [989k] links.csv
│ ├── [183k] links_small.csv
│ ├── [ 34M] movies_metadata.csv
│ ├── [710M] ratings.csv
│ └── [2.4M] ratings_small.csv
└── [1.1k] stocks
├── [151k] AAPL.csv
├── [146k] AXP.csv
├── [150k] BA.csv
├── [147k] CAT.csv
├── [146k] CSCO.csv
├── [149k] CVX.csv
├── [147k] DIS.csv
├── [ 42k] DWDP.csv
├── [150k] GS.csv
└── [...] abrdiged... In this post, we will be using three different datasets. However, if you want to limit the potential costs associated with big data analytics on AWS, you can choose to limit job submissions to only one or two of the datasets. For example, the bakery and stocks datasets are fairly small yet effectively demonstrate most EMR features. In contrast, the movie rating dataset has nearly 27 million rows of ratings data, which starts to demonstrate the power of EMR and PySpark for big data.
Step 3: Amazon EC2 key pair
According to AWS, a key pair, consisting of a private key and a public key, is a set of security credentials that you use to prove your identity when connecting to an [EC2] instance. Amazon EC2 stores the public key, and you store the private key. To SSH into the EMR cluster, you will need an Amazon key pair. If you do not have an existing Amazon EC2 key pair, create one now. The easiest way to create a key pair is from the AWS Management Console.
Your private key is automatically downloaded when you create a key pair in the console. Store your private key somewhere safe. If you use an SSH client on a macOS or Linux computer to connect to EMR, use the following chmod command to set the correct permissions of your private key file so that only you can read it.
chmod 0400 /path/to/my-key-pair.pem
Step 4: Bootstrap Script and CloudFormation Stack
The bulk of the resources that are used as part of this demonstration are created using the CloudFormation stack, emr-dem-dev. The CloudFormation template that creates the stack, cloudformation/emr-demo.yml, is included in the repository. Please review all resources and understand the cost and security implications before continuing.
There is also a JSON-format CloudFormation parameters file, cloudformation/emr-demo-params-dev.json, containing values for all but two of the parameters in the CloudFormation template. The two parameters not in the parameter file are the name of the EC2 key pair you just created and the bootstrap bucket’s name. Both will be passed along with the CloudFormation template using the Python script, create_cfn_stack.py. For each type of environment, such as Development, Test, and Production, you could have a separate CloudFormation parameters file, with different configurations.
The template will create approximately (39) AWS resources, including a new AWS VPC, a public subnet, an internet gateway, route tables, a 3-node EMR v6.2.0 cluster, a series of Amazon S3 buckets, AWS Glue data catalog, AWS Glue crawlers, several Systems Manager Parameter Store parameters, and so forth.
The CloudFormation template includes the location of the EMR bootstrap script located on Amazon S3. Before creating the CloudFormation stack, the Python script creates an S3 bootstrap bucket and copies the bootstrap script, bootstrap_actions.sh, from the local project repository to the S3 bucket. The script will be used to install additional packages on EMR cluster nodes, which are required by our PySpark applications. The script also sets the default AWS Region for boto3.
From the GitHub repository’s local copy, run the following command, which will execute a Python script to create the bootstrap bucket, copy the bootstrap script, and provision the CloudFormation stack. You will need to pass the name of your EC2 key pair to the script as a command-line argument.
python3 ./scripts/create_cfn_stack.py \
--environment dev \
--ec2-key-name <my-key-pair-name>
The CloudFormation template should create a CloudFormation stack, emr-demo-dev, as shown below.
Step 5: SSH Access to EMR
For this demonstration, we will need access to the new EMR cluster’s Master EC2 node, using SSH and your key pair, on port 22. The easiest way to add a new inbound rule to the correct AWS Security Group is to use the AWS Management Console. First, find your EC2 Security Group named ElasticMapReduce-master.
Then, add a new Inbound rule for SSH (port 22) from your IP address, as shown below.
Alternately, you could use the AWS CLI or AWS SDK to create a new security group ingress rule.
export EMR_MASTER_SG_ID=$(aws ec2 describe-security-groups | \
jq -r '.SecurityGroups[] | select(.GroupName=="ElasticMapReduce-master").GroupId')
aws ec2 authorize-security-group-ingress \
--group-id ${EMR_MASTER_SG_ID} \
--protocol tcp \
--port 22 \
--cidr $(curl ipinfo.io/ip)/32
Step 6: Raw Data and PySpark Apps to S3
As part of the emr-demo-dev CloudFormation stack, we now have several new Amazon S3 buckets within our AWS Account. The naming conventions and intended usage of these buckets follow common organizational patterns for data lakes. The data buckets use the common naming convention of raw, processed, and analyzed data in reference to the data stored within them. We also use a widely used, corresponding naming convention of ‘bronze’, ‘silver’, and ‘gold’ when referring to these data buckets as parameters.
> aws s3api list-buckets | \
jq -r '.Buckets[] | select(.Name | startswith("emr-demo-")).Name'
emr-demo-raw-123456789012-us-east-1
emr-demo-processed-123456789012-us-east-1
emr-demo-analyzed-123456789012-us-east-1
emr-demo-work-123456789012-us-east-1
emr-demo-logs-123456789012-us-east-1
emr-demo-glue-db-123456789012-us-east-1
emr-demo-bootstrap-123456789012-us-east-1
There is a raw data bucket (aka bronze) that will contain the original CSV files. There is a processed data bucket (aka silver) that will contain data that might have had any number of actions applied: data cleansing, obfuscation, data transformation, file format changes, file compression, and data partitioning. Finally, there is an analyzed data bucket (aka gold) that has the results of the data analysis. We also have a work bucket that holds the PySpark applications, a logs bucket that holds EMR logs, and a glue-db bucket to hold the Glue Data Catalog metadata.
Whenever we submit PySpark jobs to EMR, the PySpark application files and data will always be accessed from Amazon S3. From the GitHub repository’s local copy, run the following command, which will execute a Python script to upload the approximately (38) Kaggle dataset CSV files to the raw S3 data bucket.
python3 ./scripts/upload_csv_files_to_s3.py
Next, run the following command, which will execute a Python script to upload a series of PySpark application files to the work S3 data bucket.
python3 ./scripts/upload_apps_to_s3.py
Step 7: Crawl Raw Data with Glue
The last preliminary step to prepare the EMR demonstration environment is to catalog the raw CSV data into an AWS Glue data catalog database, using one of the two Glue Crawlers we created. The three kaggle dataset’s data will reside in Amazon S3, while their schema and metadata will reside within tables in the Glue data catalog database, emr_demo. When we eventually query the data from our PySpark applications, we will be querying the Glue data catalog’s database tables, which reference the underlying data in S3.
From the GitHub repository’s local copy, run the following command, which will execute a Python script to run the Glue Crawler and catalog the raw data’s schema and metadata information into the Glue data catalog database, emr_demo.
python3 ./scripts/crawl_raw_data.py --crawler-name emr-demo-raw
Once the crawler is finished, from the AWS Console, we should see a series of nine tables in the Glue data catalog database, emr_demo, all prefixed with raw_. The tables hold metadata and schema information for the three CSV-format Kaggle datasets loaded into S3.
Alternately, we can use the glue get-tables AWS CLI command to review the tables.
> aws glue get-tables --database emr_demo | \
jq -r '.TableList[] | select(.Name | startswith("raw_")).Name'
raw_bakery
raw_credits_csv
raw_keywords_csv
raw_links_csv
raw_links_small_csv
raw_movies_metadata_csv
raw_ratings_csv
raw_ratings_small_csv
raw_stocks
PySpark Applications
Let’s explore four methods to run PySpark applications on EMR.
1. Add Job Flow Steps to an Existing EMR Cluster
We will start by looking at running PySpark applications using EMR Steps. According to AWS, we can use Amazon EMR steps to submit work to the Spark framework installed on an EMR cluster. The EMR step for PySpark uses a spark-submit command. According to Spark’s documentation, the spark-submit script, located in Spark’s bin directory, is used to launch applications on a [EMR] cluster. A typical spark-submit command we will be using resembles the following example. This command runs a PySpark application in S3, bakery_sales_ssm.py.
We will target the existing EMR cluster created by CloudFormation earlier to execute our PySpark applications using EMR Steps. We have two sets of PySpark applications. The first set of three PySpark applications will transform the raw CSV-format datasets into Apache Parquet, a more efficient file format for big data analytics. Alternately, for your workflows, you might prefer AWS Glue ETL Jobs, as opposed to PySpark on EMR, to perform nearly identical data processing tasks. The second set of four PySpark applications perform data analysis tasks on the data.
There are two versions of each PySpark application. Files with suffix _ssm use the AWS Systems Manager (SSM) Parameter Store service to obtain dynamic parameter values at runtime on EMR. Corresponding non-SSM applications require those same parameter values to be passed on the command line when they are submitted to Spark. Therefore, these PySpark applications are not tightly coupled to boto3 or the SSM Parameter Store. We will use _ssm versions of the scripts in this post’s demonstration.
> tree pyspark_apps --si -v -A
pyspark_apps
├── [ 320] analyze
│ ├── [1.4k] bakery_sales.py
│ ├── [1.5k] bakery_sales_ssm.py
│ ├── [2.6k] movie_choices.py
│ ├── [2.7k] movie_choices_ssm.py
│ ├── [2.0k] movies_avg_ratings.py
│ ├── [2.3k] movies_avg_ratings_ssm.py
│ ├── [2.2k] stock_volatility.py
│ └── [2.3k] stock_volatility_ssm.py
└── [ 256] process
├── [1.1k] bakery_csv_to_parquet.py
├── [1.3k] bakery_csv_to_parquet_ssm.py
├── [1.3k] movies_csv_to_parquet.py
├── [1.5k] movies_csv_to_parquet_ssm.py
├── [1.9k] stocks_csv_to_parquet.py
└── [2.0k] stocks_csv_to_parquet_ssm.py We will start by executing the three PySpark processing applications. They will convert the CSV data to Parquet. Below, we see an example of one of the PySpark applications we will run, bakery_csv_to_parquet_ssm.py. The PySpark application will convert the Bakery Sales dataset’s CSV file to Parquet and write it to S3.
The three PySpark data processing application’s spark-submit commands are defined in a separate JSON-format file, job_flow_steps_process.json, a snippet of which is shown below. The same goes for the four analytics applications.
Using this pattern of decoupling the Spark job command and arguments from the execution code, we can define and submit any number of Steps without changing the Python script, add_job_flow_steps_process.py, shown below. Note line 31, where the Steps are injected into the add_job_flow_steps method’s parameters.
The Python script used for this task takes advantage of AWS Systems Manager Parameter Store parameters. The parameters were placed in the Parameter Store, within the /emr_demo path, by CloudFormation. We will reference these parameters in several scripts throughout the post.
> aws ssm get-parameters-by-path --path '/emr_demo' | \
jq -r ".Parameters[] | {Name: .Name, Value: .Value}"
From the GitHub repository’s local copy, run the following command, which will execute a Python script to load the three spark-submit commands from JSON-format file, job_flow_steps_process.json, and run the PySpark processing applications on the existing EMR cluster.
python3 ./scripts/add_job_flow_steps.py --job-type process
While the three Steps are running concurrently, the view from the Amazon EMR Console’s Cluster Steps tab should look similar to the example below.
Once the three Steps have been completed, we should note three sub-directories in the processed data bucket containing Parquet-format files.
Of special note is the Stocks dataset, which has been converted to Parquet and partitioned by stock symbol. According to AWS, by partitioning your data, we can restrict the amount of data scanned by each query by specifying filters based on the partition, thus improving performance and reducing cost.
Lastly, the movie ratings dataset has been divided into sub-directories, based on the schema of each table. Each sub-directory contains Parquet files specific to that unique schema.
Crawl Processed Data with Glue
Similar to the raw data earlier, catalog the newly processed Parquet data into the same AWS Glue data catalog database using one of the two Glue Crawlers we created. Similar to the raw data, earlier, processed data will reside in the Amazon S3 processed data bucket while their schemas and metadata will reside within tables in the Glue data catalog database, emr_demo.
From the GitHub repository’s local copy, run the following command, which will execute a Python script to run the Glue Crawler and catalog the processed data’s schema and metadata information into the Glue data catalog database, emr_demo.
python3 ./scripts/crawl_raw_data.py --crawler-name emr-demo-processed
Once the crawler has finished successfully, using the AWS Console, we should see a series of nine tables in the Glue data catalog database, emr_demo, all prefixed with processed_. The tables represent the three kaggle dataset’s contents converted to Parquet and correspond to the equivalent tables with the raw_ prefix.
Alternately, we can use the glue get-tables AWS CLI command to review the tables.
> aws glue get-tables --database emr_demo | \
jq -r '.TableList[] | select(.Name | startswith("processed_")).Name'
processed_bakery
processed_credits
processed_keywords
processed_links
processed_links_small
processed_movies_metadata
processed_ratings
processed_ratings_small
processed_stocks
2. Run PySpark Jobs from EMR Master Node
Next, we will explore how to execute PySpark applications remotely on the Master node on the EMR cluster using boto3 and SSH. Although this method may be optimal for certain use cases as opposed to using the EMR SDK, remote SSH execution does not scale as well in my opinion due to a lack of automation, and it exposes some potential security risks.
There are four PySpark applications in the GitHub repository. For this part of the demonstration, we will just submit the bakery_sales_ssm.py application. This application will perform a simple analysis of the bakery sales data. While the other three PySpark applications use AWS Glue, the bakery_sales_ssm.py application reads data directly from the processed data S3 bucket.
The application writes its results into the analyzed data S3 bucket, in both Parquet and CSV formats. The CSV file is handy for business analysts and other non-technical stakeholders who might wish to import the results of the analysis into Excel or business applications.
Earlier, we created an inbound rule to allow your IP address to access the Master node on port 22. From the EMR Console’s Cluster Summary tab, note the command necessary to SSH into the Master node of the EMR cluster.
The Python script, submit_spark_ssh.py, shown below, will submit the PySpark job to the EMR Master Node, using paramiko, a Python implementation of SSHv2. The script is replicating the same functionality as the shell-based SSH command above to execute a remote command on the EMR Master Node. The spark-submit command is on lines 36–38, below.
From the GitHub repository’s local copy, run the following command, which will execute a Python script to submit the job. The script requires one input parameter, which is the path to your EC2 key pair (e.g., ~/.ssh/my-key-pair.pem)
python3 ./scripts/submit_spark_ssh.py \
--ec2-key-path </path/to/my-key-pair.pem>
The spark-submit command will be executed remotely on the EMR cluster’s Master node over SSH. All variables in the commands will be replaced by the environment variables, set in advance, which use AWS CLI emr and ssm commands.
Monitoring Spark Jobs
We set spark.yarn.submit.waitAppCompletion to true. According to Spark’s documentation, this property controls whether the client waits to exit in YARN cluster mode until the application is completed. If set to true, the client process will stay alive, reporting the application’s status. Otherwise, the client process will exit after submission. We can watch the job’s progress from the terminal.
We can also use the YARN Timeline Server and the Spark History Server in addition to the terminal. Links to both are shown on both the EMR Console’s Cluster ‘Summary’ and ‘Application user interfaces’ tabs. Unlike other EMR application web interfaces, using port forwarding, also known as creating an SSH tunnel, is not required for the YARN Timeline Server or the Spark History Server.
YARN Timeline Server
Below, we see that the job we submitted running on the YARN Timeline Server also includes useful tools like access to configuration, local logs, server stacks, and server metrics.
YARN Timeline Server allows us to drill down into individual jobs and view logs. Logs are ideal for troubleshooting failed jobs, especially the stdout logs.
Spark History Server
You can also view the PySpark application we submitted from the Master node using the Spark History Server. Below, we see completed Spark applications (aka Spark jobs) in the Spark History Server.
Below, we see more details about our Spark job using the Spark History Server.
We can even see visual representations of each Spark job’s Directed Acyclic Graph (DAG).
3. Run Job Flow on an Auto-Terminating EMR Cluster
The next option to run PySpark applications on EMR is to create a short-lived, auto-terminating EMR cluster using the run_job_flow method. We will create a new EMR cluster, run a series of Steps (PySpark applications), and then auto-terminate the cluster. This is a cost-effective method of running PySpark applications on-demand.
We will create a second 3-node EMR v6.2.0 cluster to demonstrate this method, using Amazon EC2 Spot instances for all the EMR cluster’s Master and Core nodes. Unlike the first, long-lived, more general-purpose EMR cluster, we will only deploy the Spark application to this cluster as that is the only application we will need to run the Steps.
Using the run_job_flow method, we will execute the four PySpark data analysis applications. The PySpark application’s spark-submit commands are defined in a separate JSON-format file, job_flow_steps_analyze.json. Similar to the previous add_job_flow_steps.py script, this pattern of decoupling the Spark job command and arguments from the execution code, we can define and submit any number of Steps without changing the Python execution script. Also similar, this script retrieves parameter values from the SSM Parameter Store.
From the GitHub repository’s local copy, run the following command, which will execute a Python script to create a new cluster, run the two PySpark applications, and then auto-terminate.
python3 ./scripts/run_job_flow.py --job-type analyze
As shown below, we see the short-lived EMR cluster in the process of terminating after successfully running the PySpark applications as EMR Steps.
4. Using AWS Step Functions
According to AWS, AWS Step Functions is a serverless function orchestrator that makes it easy to sequence AWS Lambda functions and multiple AWS services. Step Functions manages sequencing, error handling, retry logic, and state, removing a significant operational burden from your team. Step Functions is based on state machines and tasks. A state machine is a workflow. A task is a state in a workflow that represents a single unit of work that another AWS service performs. Each step in a workflow is a state. Using AWS Step Functions, we define our workflows as state machines, which transform complex code into easy to understand statements and diagrams.
You can use AWS Step Functions to run PySpark applications as EMR Steps on an existing EMR cluster. Using Step Functions, we can also create the cluster, run multiple EMR Steps sequentially or in parallel, and finally, auto-terminate the cluster.
We will create two state machines for this demo, one for the PySpark data processing applications and one for the PySpark data analysis applications. To create state machines, we first need to create JSON-based state machine definition files. The files are written in Amazon States Language. According to AWS, Amazon States Language is a JSON-based, structured language used to define a state machine, a collection of states that can do work (Task states), determine which states to transition to next (Choice states), stop execution with an error (Fail states), and so on.
The definition files contain specific references to AWS resources deployed to your AWS account originally created by CloudFormation. Below is a snippet of the state machine definition file, step_function_emr_analyze.json, showing part of the configuration of the EMR cluster. Note the parameterized key/value pairs (e.g., “Ec2KeyName.$”: “$.InstancesEc2KeyName” on line 5). The values will come from a JSON-formatted inputs file and are dynamically replaced upon the state machine’s execution.
Python Templating
To automate the process of adding dynamic resource references to the state machine’s inputs files, we will use Jinja, the modern and designer-friendly templating language for Python, modeled after Django’s templates. We will render the Jinja template to a JSON-based state machine inputs file, replacing the template’s resource tags (keys) with values from the SSM Parameter Store’s parameters. Below is a snippet from the inputs file Jinja template, step_function_inputs_analyze.j2.
First, install Jinja2, then create two JSON-based state machine inputs files from the Jinja templates using the included Python file.
# install Jinja2 python3 -m pip install Jinja2 python3 ./scripts/create_inputs_files.py
Below we see the same snippet of the final inputs file. Jinja tags have been replaced with values from the SSM Parameter Store.
Using the definition files, create two state machines using the included Python files.
python3 ./scripts/create_state_machine.py \
--definition-file step_function_emr_process.json \
--state-machine EMR-Demo-Process
python3 ./scripts/create_state_machine.py \
--definition-file step_function_emr_analyze.json \
--state-machine EMR-Demo-Analysis
Both state machines should appear in the AWS Step Functions Console’s State Machines tab. Below, we see the ‘EMR-Demo-Analysis’ state machine’s definition both as JSON and rendered visually to a layout.
To execute either of the state machines, use the included Python file, passing in the exact name of the state machine to execute, either ‘EMR-Demo-Process’ or ‘EMR-Demo-Analysis’, and the name of the inputs file. I suggest running the EMR-Demo-Analysis version so as not to re-process all the raw data.
python3 ./scripts/execute_state_machine.py \
--state-machine EMR-Demo-Process \
--inputs-file step_function_inputs_process.json
python3 ./scripts/execute_state_machine.py \
--state-machine EMR-Demo-Analysis \
--inputs-file step_function_inputs_analyze.json
When the PySpark analysis application’s Step Function state machine is executed, a new EMR cluster is created, the PySpark applications are run, and finally, the cluster is auto-terminated. Below, we see a successfully executed state machine, which successfully ran the four PySpark analysis applications in parallel, on a new auto-terminating EMR cluster.
Conclusion
This post explored four methods for running PySpark applications on Amazon Elastic MapReduce (Amazon EMR). The key to scaling data analytics with PySpark on EMR is the use of automation. Therefore, we looked at ways to automate the deployment of EMR resources, create and submit PySpark jobs, and terminate EMR resources when the jobs are complete. Furthermore, we were able to decouple references to dynamic AWS resources within our PySpark applications using parameterization. This allows us to deploy and run PySpark resources across multiple AWS Accounts and AWS Regions without code changes.
In part two of the series, we will explore the use of the recently announced service, Amazon Managed Workflows for Apache Airflow (MWAA), and in part three, the use of Juypter and Zeppelin notebooks for data science, scientific computing, and machine learning on EMR.
This blog represents my own viewpoints and not of my employer, Amazon Web Services. All product names, logos, and brands are the property of their respective owners.
GTM Stack: Exploring IoT Data Analytics at the Edge with Grafana, Mosquitto, and TimescaleDB on ARM-based Architectures
Posted by Gary A. Stafford in Big Data, IoT, Python, Raspberry Pi, Software Development on October 12, 2020
In the following post, we will explore the integration of several open-source software applications to build an IoT edge analytics stack, designed to operate on ARM-based edge nodes. We will use the stack to collect, analyze, and visualize IoT data without first shipping the data to the Cloud or other external systems.
The Edge
Edge computing is a fast-growing technology trend, which involves pushing compute capabilities to the edge. Wikipedia describes edge computing as a distributed computing paradigm that brings computation and data storage closer to the location needed to improve response times and save bandwidth. The term edge commonly refers to a compute node at the edge of a network (edge device), sitting in close proximity to the source a data and between that data source and external system such as the Cloud.
In his recent post, 3 Advantages (And 1 Disadvantage) Of Edge Computing, well-known futurist Bernard Marr argues reduced bandwidth requirements, reduced latency, and enhanced security and privacy as three primary advantages of edge computing. Due to techniques like data downsampling, Marr advises one potential disadvantage of edge computing is that important data could end up being overlooked and discarded in the quest to save bandwidth and reduce latency.
David Ricketts, Head of Marketing at Quiss Technology PLC, estimates in his post, Cloud and Edge Computing — The Stats You Need to Know for 2018, the global edge computing market is expected to reach USD 6.72 billion by 2022 at a compound annual growth rate of a whopping 35.4 percent. Realizing the market potential, many major Cloud providers, edge device manufacturers, and integrators are rapidly expanding their edge compute capabilities. AWS, for example, currently offers more than a dozen services in the edge computing category.
Internet of Things
Edge computing is frequently associated with the Internet of Things (IoT). IoT devices, industrial equipment, and sensors generate data, which is transmitted to other internal and external systems, often by way of edge nodes, such as an IoT Gateway. IoT devices typically generate time-series data. According to Wikipedia, a time series is a set of data points indexed in time order — a sequence taken at successive equally spaced points in time. IoT devices typically generate continuous high-volume streams of time-series data, often on the scale of millions of data points per second. IoT data characteristics require IoT platforms to minimally support temporal accuracy, high-volume ingestion and processing, efficient data compression and downsampling, and real-time querying capabilities.
The IoT devices and the edge devices, such as IoT Gateways, which aggregate and transmit IoT data from these devices to external systems, are generally lower-powered, with limited processor, memory, and storage capabilities. Accordingly, IoT platforms must satisfy all the requirements of IoT data while simultaneously supporting resource-constrained environments.
IoT Analytics at the Edge
Leading Cloud providers AWS, Azure, Google Cloud, IBM Cloud, Oracle Cloud,