Automatized Code Deployment from Azure DevOps to Databricks

Target audience are data practitioners looking for a method to practice DataOps with a simple method even in restricted environments. A walk-through of the code is detailed in the appendix.

The linked code repository contains a minimal setup to automatize infrastructure and code deployment simultaneously from Azure DevOps Git Repositories to Databricks.

TL;DR:

  1. Import the repo into a fresh Azure DevOps Project,
  2. get a secret access token from your Databricks Workspace,
  3. paste the token and the Databricks URL into a Azure DevOps Library’s variable group named “databricks_cli”,
  4. Create and run two pipelines referencing the YAML in the repo’s pipelines/ directory.
  5. Any Databricks compatible (Python, Scala, R) code pushed to the remote repository’s workspace/ directory will be copied to the Databricks workspace with an interactive cluster waiting to execute it.

Background

Azure DevOps and Databricks have one thing in common – providing industry standard technology and offering them as an intuitive, managed platform:

  • Databricks for running Apache Spark
  • DevOps for Git repos and build pipelines

Both platforms have much more to offer then what is used in this minimal integration example. DevOps offers wiki, bug-, task- and issue tracking, canban, scrum and workflow functionality among others.

Databricks is a fully managed and optimized Apache Spark PaaS. It can natively execute Scala, Python, PySpark, R, SparkR, SQL and Bash code; some cluster types have Tensorflow installed and configured (inclusive GPU drivers). Integration of the H2O machine learning platform is quite straight forward. In essence Databricks is a highly performant general purpose data science and engineering platform which tackles virtually any challenge in the Big Data universe.

Both have free tiers and a pay-as-you-go pricing model.

Databricks provides infrastructure as code. A few lines of JSON consistently deploy an optimized Apache Spark runtime.

After several projects and the increasing need to build and prototype in a managed and reproducible way the DevOps-Databricks combination became very appreciated: It enables quick and responsive interactive runtimes and provides best industry practice for software development and data engineering. Deployment into (scheduled), performant, resilient production environments is possible without changes to the platform and without any need for refactoring.

The core of the integration uses Databricks infrastructure-as-code (IaC) capability together with DevOps pipelines functionality to deploy any kind of code.

  1. the Databricks CLI facilitates programmatic access to Databricks and
  2. the managed Build Agents in DevOps deploy both infrastructure and analytic code.

Azure pipelines deploy both the infrastructure code and the notebook code from the repository to the Databricks workspace. This enables version control of both the runtime and the code in one compact, responsive repository.

All pieces of the integration are hosted in a single, compact repository which make all parts of a data and modeling pipeline fully reproducible.

Prerequisites

Log into Azure DevOps and Databricks Workspace. There are free tiers for both of them. Setup details are explained extensively in the canonical quick start sections of either service:

For the integration Databricks can be hosted in either the Azure or AWS cloud.

1. Import the Repository

To use this demo as a starting point for a new project, prepare a Azure DevOps project:

  • create a new project (with an empty repository by default)
  • select the repository tab and choose “Import a repository”
  • paste the URL of this demo into the Clone URL field: https://dev.azure.com/reinhardseifert/DatabricksDevOps/_git/DatabricksDevOps
  • wait for the import to complete
  • clone the newly imported repository to your local computer to start deploying your own code into the workspace directory

Then create two Azure pipelines which create the runtime and sync any code updates into it (see below).

2. Create Databricks Secret Token

Log into the Databricks Workspace and under User settings (icon in the top right corner) and select “Generate New Token”. Choose a descriptive name (“DevOps Build Agent Key”) and copy the token to a notebook or clipboard. The token is displayed just once – directly after creation; you can create as many tokens as you wish.

Databricks > User Settings > Create New Token

3. Add the token to the Azure DevOps Library

The Databricks Secret Token has to be added to a Variable Group named “databricks_cli”. Variable groups are created under Pipelines > Library. Note that the name of the variable group is referenced in both pipeline definitions (/pipelines/build-cluster.yml and /pipelines/build-workspace.yml). Two variables have to be defined: 1. databricks_host and 2. databricks_token

The variable names are referenced in the .yml file – changing them in the DevOps library requires also changing them correspondingly in the .yml files. When clicking the lock icon after defining the variable it is treated as a secret and not visible after that action in the DevOps project. Neither in the Library nor in the Build servers (even when accidentially echo-ing them. But of course writing them to the Databricks environment would potentially expose them. This is a security concern when collaborating with non-trusted parties on a Project.

Pipelines > Library > Add Variable Group

 

Azure DevOps

Generally the Azure DevOps portal offers as minimal functionality a git repository to maintain code and pipelines to deploy the code from the repository into runtimes.

Azure Repositories

The Azure repo contains the full logic of the integration:

  1. the actual (Python) code to run,
  2. the JSON specification of the Spark-cluster which will run the code,
  3. shell build scripts which are executed in the pipeline/ build server,
  4. the YAML configuration which define the pipelines.

The complete CI/CD pipeline is contained in a single Git repository in a very compact fashion. Following Databricks’ terminology the Python code (1) is located in the workspace/ directory. The runtime specification .json (2), build scripts .sh (3) and the pipeline configuration .yml (4) are located in the pipelines/ directory according to the Azure DevOps paradigm.

Azure Pipelines

The Pipelines menu provides the following functionality:

  • Pipelines (aka build pipelines),
  • Environments (needed to group Azure resources – not used here),
  • Releases (aka release pipelines – not used here)
  • Library (containing the variable groups)

The build pipelines exclusively used in this demo project are managed under the “Pipelines > Pipelines” menu tab – not really intuitive.

Azure Build Pipelines

The pipeline’s build agents are configured via YAML files (e.g. build-cluster.yml). In this case they install the Databricks CLI on the build machine and then execute CLI commands to create runtimes and move code notebooks to the runtime. The Databricks cluster is configured by a single JSON file (see config.cluster.json).

This minimal integration requires creation of two pipelines:

  1. cluster creation – referencing pipelines/build-cluster.yml and
  2. workspace synchronization – referencing /pipelines/build-workspace.yml

After importing the repo:

  • select the Pipelines > Pipelines menu tab
  • choose Azure Repos Git YAML
  • select the imported repository from the drop-down menu
  • select Existing Azure Pipeline YAML file
  • select the YAML file from the drop-down menu
  • Run the pipeline for the first time – or just save it and run it later.

At this point the Databricks secret access token mentioned in the prerequisite paragraph need to be present in a “databricks_cli” variable group. Otherwise the pipeline run will fail and warn; in this case just create the token (in Databricks) and the variable group (in DevOps) and re-run the pipeline.

After creating the pipelines and saving them (or running them initially), the default pipeline names reference the source repository name which triggers them. For easier monitoring the pipelines should be renamed according to their function, like “create-cluster” and “sync-workspace” in this case.

Summary

This concludes the integration of analytic code from an Azure DevOps repository into a hosted Databricks runtime.

Any change to the config.cluster.json deletes the existing cluster and creates a new one according to the specifications in the JSON file.

Any change to workspace/ will copy the notebook file(s) (R, Python, Scala) to the Databricks workspace for execution on the cluster.

The Databricks workspace in this example was hosted on Azure. Only minor changes are required to use an AWS hosted workspace. On all cloud platforms the host URL and security token is specific for the chosen instance and region. The cloud specific parameter is the node_type_id in the cluster configuration .json file.

Using this skeleton repo as a starting point, it is immediately possible to run interactive workloads on a performant Apacke Spark cloud cluster – instead of “cooking” the local laptop with analytic code – transparently maintained on a professional DevOps platform.

Appendix

Following, a detailed walk-through of the .yml pipeline configurations, .sh build scripts and .json configuration files.

In general, the YAML instructs the build server to 1. start up when a certain file is changed (trigger), 2. copy the contents of the repository to the build server and 3. execute a selection of shell scripts (tasks) from the repository

Pipeline: Create cluster

This is a detailed walk through for the build-cluster.yml pipeline. The .yml files have a hierachical structure and the full hierarchy of the DevOps build pipeline is included although stages could be omitted.

Trigger

The first section of the pipeline YAML specifies the trigger. Any changes to the specified branch of the linked repo will automatically run of the Build Agent.

trigger:
  branches:
    include:
    - master
  paths:
    include:
    - pipelines/config.cluster.json
    - pipelines/databricks-library-install.sh

Without the paths: section, any change to the master branch will run the pipeline. The cluster is rebuild when the configuration changes or the selection of installed Python- or R-libraries changes.

Stages

The stage can be omitted (for a single stage pipeline) and the pool, variables and jobs directly defined. Then the stage would be implicit. It is possible to add testing steps to the pipeline and build fully automated CI/CD pipelines accross environments within on .yml file.

stages:
- stage: "dev"
  displayName: "Development"
  dependsOn: []

Pool

  pool:
    vmImage: "ubuntu-latest"

Selects the type of virtual machine to start when the trigger files are changed. At the time of writing ubuntu_latest will start a Ubuntu 18.04 LTS image.

Variables

  variables:
    - group: databricks_cli

This section references the variable group created in the Prerequisite section. The secret token is transfered to the build server and authorizes the API calls from the server to the Databricks workspace.

Jobs, Steps and Tasks

A job is a sequence of steps which are executed on the build server (pool). In this pipeline only task steps are used (see the docs for all step operations).

  jobs:
    - job: CreateCluster4Dev
      steps:

        - task: UsePythonVersion@0
          inputs:
            versionSpec: "3.8"
            architecture: "x64"

The first step is selecting the Python version for all following Python command on the build server; the Databricks CLI is written in Python and installed via Pip in the following task.

Task: Install and configure the Databricks CLI

        - task: ShellScript@2
          inputs:
            scriptPath: pipelines/databricks-cli-config.sh
            args: "\$(databricks_host) \$(databricks\_token)"
          displayName: "Install and configure the Databricks CLI"

Note that the path is relative to the root of the repo. The secret access token and host URL from the DevOps library are copied into environment variables which can be passed to the script in the args section.

The shell script executes the installation of the Databricks CLI and writes the neccessary CLI configuration on the build server.

python -m pip install databricks-cli
echo -e "[DEFAULT]\nhost: $HOST\ntoken: $TOKEN" > $HOME/.databrickscfg

Task: “Delete previous cluster version (if existing)”

This task will remove any cluster with the name provided in the args: section. This allows for updating the cluster when the configuration file is changed. When no such cluster is present the script will fail. Usually the pipeline will break at this point but here continueOnError is true, so the pipeline will continue when creating a cluster for the first time.

        - task: ShellScript@2
          inputs:
            scriptPath: pipelines/databricks-cluster-delete.sh
            args: "HelloCluster"
          continueOnError: "true"
          displayName: "Delete previous cluster version (if existing)"

The shell script called by this task is a wrapper around the Databricks CLI. First it queries for the cluster-id of any cluster with the name passed.

CLUSTER_ID=$(databricks clusters list --output json | jq -r --arg N "$CLUSTER_NAME" '.clusters[] | select(.cluster_name == $N) | .cluster_id')

It is possible to create multiple clusters with the same name. In case there are multiple all of them are deleted.

for ID in $CLUSTER_ID
do
    echo "Deleting $ID"
    databricks clusters permanent-delete --cluster-id $ID
done

Task: Create new cluster

        - task: ShellScript@2
          inputs:
            scriptPath: pipelines/databricks-cluster-create.sh
            args: "HelloCluster"
          displayName: "Create new cluster"

The build script reads the config.cluster.json and adds the cluster name passed from the pipeline .yml

cat config.cluster.json | sed "s/CLUSTER_NAME/$CLUSTER_NAME/g" > /tmp/conf.json

Now the configuration .json file can be passed to the Databricks CLI. The complete Apache Spark infrastructure is configured in the json. CLUSTER_NAME will be replaced with the name passed from the .yml.

{
    "cluster_name": "CLUSTER_NAME",
    "spark_version": "6.0.x-scala2.11",
    "spark_conf": {
        "spark.sql.execution.arrow.enabled": "true"
    },
    "node_type_id": "Standard_DS3_v2",
    "num_workers": 1,
    "ssh_public_keys": [],
    "custom_tags": {
        "Project": "DevOpsIntegration"
    },
    "cluster_log_conf": {
        "dbfs": {
            "destination": "dbfs:/cluster_logs"
        }
    },
    "spark_env_vars": {
        "PYSPARK_PYTHON": "/databricks/python3/bin/python3"
    },
    "autotermination_minutes": 120,
    "enable_elastic_disk": false,
    "init_scripts": []
}

Updating the runtime to another version requires only modifying the spark_version parameter with any supported runtime.

A Spark cluster consists of one driver node and a number of worker nodes and can be scaled horizontally by adding nodes (num_workers) or vertically by choosing larger node types. The node types are cloud provider specific. The Standard_DS3_v2 node type id references the minimal Azure node.

The autotermination feature shuts the cluster down when not in use. Costs are billed per second up time per processing unit.

Any reconfigurations triggers the pipeline and rebuilds the cluster.

CLUSTER_ID=$(databricks clusters create --json-file /tmp/conf.json | jq -r '.cluster_id')

The cluster create call returns the cluster-id of the newly created instance. Since the last step of this pipeline installs additional Python and R libraries (via Pip and CRAN respectively) it is necessary to wait for the cluster to be in pending state.

STATE=$(databricks clusters list --output json | jq -r --arg I "$CLUSTER_ID" '.clusters[] | select(.cluster_id == $I) | .state')

echo "Wait for cluster to be PENDING"
while [[ "$STATE" != "PENDING" ]]
do
    STATE=$(databricks clusters list --output json | jq -r --arg I "$CLUSTER_ID" '.clusters[] | select(.cluster_name == $I) | .state')
done

Task: Install Python and R dependencies on the cluster

The final step is to add additional Python and R packages to the cluster. There are many ways to install packges in Databricks. This is just one way to do it.

        - task: ShellScript@2
          inputs:
            scriptPath: pipelines/databricks-library-install.sh
            args: "HelloCluster"
          displayName: "Install Python and R dependencies"

Again the shell script wraps the Databricks CLI, here the library install command. The cluster name (“DemoCluster” in this example) has to be passed again.

All CLI calls to Databricks need the cluster-id to delete, create and manupulate instances. So first fetch it with a cluster list call:

CLUSTER_ID=$(databricks clusters list --output json | jq -r --arg N "$CLUSTER_NAME" '.clusters[] | select(.cluster_name == $N) | .cluster_id')

Then install the packages – one call to library install per package:

databricks libraries install --cluster-id $CLUSTER_ID --pypi-package azure
databricks libraries install --cluster-id $CLUSTER_ID --pypi-package googlemaps
databricks libraries install --cluster-id $CLUSTER_ID --pypi-package python-tds
databricks libraries install --cluster-id $CLUSTER_ID --cran-package tidyverse

For additional Python or R package add a line in this build script – this will trigger the pipeline and the cluster is rebuild.

Pipeline: Import workspace

This is a detailed walk through for the build-workspace.yml pipeline. The first part of the pipeline is identical to the build-cluster.yml pipeline. The trigger include differs, since this pipeline is triggered by code pushes to the workspace/ directory. The choice of the build server (pool), the variable reference to the databricks_cli variable group for the Databricks access tokens and the Python version task are identical, also installing and configuring the Databricks CLI with the same build script as above.

The only build task is importing all files in the workspace/ directory to the Databricks Workspace. The args passes a sub-directory name for the /Shared/ folder in Databricks ( /Shared/HelloWorkspace/ in the example).

        - task: ShellScript@2
          inputs:
            scriptPath: pipelines/databricks-workspace-import.sh
            args: "HelloWorkspace"
          displayName: "Import updated notebooks to workspace to dev"

The specified directory is first deleted. When the directory does not exist, the CLI prints and error in JSON format, but does not break the pipeline. The args: parameter is passed to the $SUBDIR variable in the build script.

databricks workspace delete --recursive /Shared/$SUBDIR

Then the script files in the workspace/ folder of the master branch are copied into the Databricks workspace.

databricks workspace import_dir ../workspace /Shared/$SUBDIR/

Remember that the repo is copied into the pipeline build agent/server and the working directory of the pipeline agent points to the location of the .yml file which defines the pipeline.

Pyspark looks like regular python code, but the distributed nature of the execution requires the whole new way of thinking to optimize the code.

PySpark execution logic and code optimization

PySpark looks like regular python code. In reality the distributed nature of the execution requires the whole new way of thinking to optimize the PySpark code.

This article will focus on understanding PySpark execution logic and performance optimization. PySpark DataFrames are in an important role.

To try PySpark on practice, get your hands dirty with this tutorial: Spark and Python tutorial for data developers in AWS

DataFrames in pandas as a PySpark prerequisite

PySpark needs totally different kind of engineering compared to regular Python code.

If you are going to work with PySpark DataFrames it is likely that you are familiar with the pandas Python library and its DataFrame class.

Here comes the first source of potential confusion: despite their similar names, PySpark DataFrames and pandas DataFrames behave very differently. It is also easy to confuse them in your code. You might want to use suffix like _pDF for pandas DataFrames and _sDF for Spark DataFrames.

The pandas DataFrame object stores all the data represented by the data frame within the memory space of the Python interpreter. All of the data is easily and immediately accessible. The operations on the data are executed immediately when the code is executed, line by line. It is easy to print intermediate results to debug the code.

However, these advantages are offset by the fact that you are limited by the local computer’s memory and processing power constraints – you can only handle data which fits into the local memory. But since the operations are done in memory, with a basic data processing task you do not need to wait more than a few minutes at maximum.

PySpark DataFrames and their execution logic

The PySpark DataFrame object is an interface to Spark’s DataFrame API and a Spark DataFrame within a Spark application. The data in the DataFrame is very likely to be somewhere else than the computer running the Python interpreter – e.g. on a remote Spark cluster running in the cloud.

There are two distinct kinds of operations on Spark DataFrames: transformations and actions. Transformations describe operations on the data, e.g. filtering a column by value, joining two DataFrames by key columns, or sorting data. Actions are operations which take DataFrame(s) as input and output something else. Some examples from action would be showing the contents of a DataFrame or writing a DataFrame to a file system.

The key point to understand how Spark works is that transformations are lazy. Executing a Python command which describes a transformation of a PySpark DataFrame to another does not actually require calculations to take place. Ordering by a column and calculating aggregate values, returning another PySpark DataFrame would be such transformation. Rather, the operation is added to the graph describing what Spark should eventually do.

When an action is requested – e.g. return the contents of this Spark DataFrame as a Pandas DataFrame – Spark looks at the processing graph and then optimizes the tasks which needs to be done. This is the job of the Catalyst optimizer, and it enables Spark to optimize the operations to very high degree.

Also, the actual computation tasks run on the Spark cluster, meaning that you can have huge amounts of memory and processing cores available for the actual computation, even without resorting to the top-of-the-line virtual machines offered by cloud providers.

Consider caching to speed up PySpark

However, the highly optimized and parallelized execution comes at a cost: it is not as easy to see what is going on at each step. Looking at the data after some transformations means that you have to gather the data, or its subset, to a single computer. This is an action, so Spark has to determine the computation graph, optimize it, and execute it.

If your dataset is large, this may take quite some time. This is especially true if caching is not enabled and Spark has to start by reading the input data from a remote source – such as a database cluster or cloud object storage like S3.

You can alleviate this by caching the DataFrame at some suitable point. Caching causes the DataFrame partitions to be retained on the executors and not be removed from memory or disk unless there is a pressing need. In practice this means that the cached version of the DataFrame is available quickly for further calculations. However, playing around with the data is still not as easy or quick as with pandas DataFrames.

Use small scripts and multiple environments in PySpark

As a rule of thumb, one PySpark script should perform just one well defined task. This is due to the fact that any action triggers the transformation plan execution from the beginning. Managing and debugging becomes a pain if the code has lots of actions.

The normal flow is to read the data, transform the data and write the data. Often the write stage is the only place where you need to execute an action. Instead of debugging in the middle of the code, you can review the output of the whole PySpark job.

With large amounts of data this approach would be slow. You would have to wait a long time to see the results after each job.

My suggestion is to create environments that have different sizes of data. In the environment with little data you test the business logic and syntax. The test cycle is rapid as there’s no need process gigabytes of data. Running the PySpark script with the full dataset reveals the performance problems.

This goes well together with the traditional dev, test, prod environment split.

Favor DataFrame over RDD with structured data

RDD (Resilient Distributed Dataset) can be any set of items. For example, a shopping list.

["apple", "milk", "bread"]

RDD is the low-level data representation in Spark, and in earlier versions of Spark it was also the only way to access and manipulate data. However, the DataFrame API was introduced as an abstraction on top of the RDD API. As a rule of thumb, unless you are doing something very involved (and you really know what you are doing!), stick with the DataFrame API.

DataFrame is a tabular structure: a collection of Columns, each of which has a well defined data type. If you have a description and amount for each item in the shopping list, then a DataFrame would do better.

+-------+-----------+------+
|product|description|amount|
+-------+-----------+------+
|apple  |green      |5     |
|milk   |skimmed    |2     |
|bread  |rye        |1     |
+-------+-----------+------+

This is also a very intuitive representation for structured data, something that can be found from a database table. PySpark DataFrames have their own methods for data manipulation just like pandas DataFrames have.

Avoid User Defined Functions in PySpark

As a beginner I thought PySpark DataFrames would integrate seamlessly to Python. That’s why I chose to use UDFs (User Defined Functions) to transform the data.

A UDF is simply a Python function which has been registered to Spark using PySpark’s spark.udf.register method.

With the small sample dataset it was relatively easy to get started with UDF functions. When running the PySpark script with more data, spark popped an OutOfMemory error.

Investigating the issue revealed that the code could not be optimized when using UDFs.  To Spark’s Catalyst optimizer, the UDF is a black box. This means that Spark may have to read in all of the input data, even though the data actually used by the UDF comes from a small fragments in the input I.e. doing data filtering at the data read step near the data, i.e. predicate pushdown, cannot be used.

Additionally, there is a performance penalty: on the Spark executors, where the actual computations take place, data has to be converted (serialized) in the Spark JVM to a format Python can read, a Python interpreter spun up, the data deserialized in the Python interpreter, the UDF executed, and the result serialized and deserialized again to the Spark JVM. All of this takes significant amounts of time!

The recommendation is to stay in native PySpark dataframe functions whenever possible, since they are translated directly to native Scala functions running on Spark.

If you absolutely, positively need to do something with UDFs in PySpark, consider using the pandas vectorized UDFs introduced in Spark 2.3 – the UDFs are still a black box to the optimizer, but at least the performance penalty of moving data between JVM and Python interpreter is lot smaller.

Number of partitions and partition size in PySpark

In order to process data in a parallel fashion on multiple compute nodes, Spark splits data into partitions, smaller data chunks. A DataFrame of 1,000,000 rows could be partitioned to 10 partitions having 100,000 rows each. Additionally, the computation jobs Spark runs are split into tasks, each task acting on a single data partition. Spark cluster has a driver that distributes the tasks to multiple executors. This means that the datasets can be much larger than fits into the memory of a single computer – as long as the partitions fit into the memory of the computers running the executors.

In one of the projects our team encountered an out-of-memory error that we spent a long time figuring out. Finally we found out that the problem was a result of too large partitions. The data in a partition could simply not fit to the memory of a single executor node.

Too few partitions also make the execution inefficient. Some of the executor cores idle while others are working on a full steam, if there are not as many partitions as there are available cores (or, technically, available slots)

However, having a large amount of small partitions is not optimal either – shuffling the data in the small partitions is inefficient. Also reading and writing to disk (not to mention a network destination) in small chunks potentially increases the total execution time.

The Spark programming guide recommends 128 MB partition size as the default. For 128 GB of data this would mean 1000 partitions. Without going too deep in the details, consider partitioning as a crucial part of the optimization toolbox. If your partitions are too large or too small, you can use the coalesce() and repartition() methods of DataFrame to instruct Spark to modify the partition distribution. The number of partitions in a DataFrame sDF can be checked with sDF.rdd.getNumPartitions().

Summary – PySpark basics and optimization

PySpark offers a versatile interface for using powerful Spark clusters, but it requires a completely different way of thinking and being aware of the differences of local and distributed execution models. The functionality offered by the core PySpark interface can be extended by creating User-Defined Functions (UDFs), but as a tradeoff the performance is not as good as for native PySpark functions due to lesser degree of optimization. Partitioning the data correctly and with a reasonable partition size is crucial for efficient execution – and as always, good planning is the key to success.

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The editor to modify the python flavored spark code.

AWS Glue tutorial with Spark and Python for data developers

This AWS Glue tutorial is a hands-on introduction to create a data transformation script with Spark and Python. Basic Glue concepts such as database, table, crawler and job will be introduced.

In this tutorial you will create an AWS Glue job using Python and Spark. You can read the previous article for a high level Glue introduction.

In the context of this tutorial Glue could be defined as “A managed service to run Spark scripts”.

In some parts of the tutorial I reference to this GitHub code repository.

Create a data source for AWS Glue

Glue can read data either from database or S3 bucket. For this tutorial I created an S3 bucket called glue-blog-tutorial-bucket. You have to come up with another name on your AWS account.

Create two folders from S3 console called read and write.

The S3 bucket has two folders. In AWS folder is actually just a prefix for the file name.
The S3 bucket has two folders. In AWS a folder is actually just a prefix for the file name.

 

Upload this movie dataset to the read folder of the S3 bucket.

The data for this python and spark tutorial in Glue contains just 10 rows of data. Source: IMDB.
The data for this Python and Spark tutorial in Glue contains just 10 rows of data. Source: IMDB.

Crawl the data source to the data catalog

Glue has a concept of crawler. A crawler sniffs metadata from the data source such as file format, column names, column data types and row count. The metadata makes it easy for others to find the needed datasets. The Glue catalog enables easy access to the data sources from the data transformation scripts.

The crawler will catalog all files in the specified S3 bucket and prefix. All the files should have the same schema.

In Glue crawler terminology the file format is known as a classifier. The crawler identifies the most common classifiers automatically including CSV, json and parquet. It would be possible to create a custom classifiers where the schema is defined in grok patterns which are close relatives of regular expressions.

Our sample file is in the CSV format and will be recognized automatically.

Instructions to create a Glue crawler:

  1. In the left panel of the Glue management console click Crawlers.
  2. Click the blue Add crawler button.
  3. Give the crawler a name such as glue-blog-tutorial-crawler.
  4. In Add a data store menu choose S3 and select the bucket you created. Drill down to select the read folder.
  5. In Choose an IAM role create new. Name the role to for example glue-blog-tutorial-iam-role.
  6. In Configure the crawler’s output add a database called glue-blog-tutorial-db.

 

Summary of the AWS Glue crawler configuration.
Summary of the AWS Glue crawler configuration.

 

When you are back in the list of all crawlers, tick the crawler that you created. Click Run crawler.

Note: If your CSV data needs to be quoted, read this.

The crawled metadata in Glue tables

Once the data has been crawled, the crawler creates a metadata table from it. You find the results from the Tables section of the Glue console. The database that you created during the crawler setup is just an arbitrary way of grouping the tables.

Metadata for the Glue table. You can see properties as well as column names and data types from this view.
Metadata for the Glue table.

 

Glue tables don’t contain the data but only the instructions how to access the data.

Note: For large CSV datasets the row count seems to be just an estimation.

AWS Glue jobs for data transformations

From the Glue console left panel go to Jobs and click blue Add job button.

Follow these instructions to create the Glue job:

  1. Name the job as glue-blog-tutorial-job.
  2. Choose the same IAM role that you created for the crawler. It can read and write to the S3 bucket.
  3. Type: Spark.
  4. Glue version: Spark 2.4, Python 3.
  5. This job runsA new script to be authored by you.
  6. Security configuration, script libraries, and job parameters
    1. Maximum capacity2. This is the minimum and costs about 0.15$ per run.
    2. Job timeout10. Prevents the job to run longer than expected.
  7. Click Next and then Save job and edit the script.

Editing the Glue script to transform the data with Python and Spark

Copy this code from Github to the Glue script editor.

Remember to change the bucket name for the s3_write_path variable.

Save the code in the editor and click Run job.

The Glue editor to modify the python flavored spark code.
The Glue editor to modify the python flavored Spark code.

 

The detailed explanations are commented in the code. Here is the high level description:

  1. Read the movie data from S3
  2. Get movie count and rating average for each decade
  3. Write aggregated data back to S3

The execution time with 2 Data Processing Units (DPU) was around 40 seconds. Relatively long duration is explained by the start-up overhead.

The data transformation creates summarized movie data. For example 90's had 4 movies in the top 10 with the average score of 8.95.
The data transformation script creates summarized movie data. For example 1990 decade had 4 movies in the IMDB top 10 with the average score of 8.95.

 

You can download the result file from the write folder of your S3 bucket. Another way to investigate the job would be to take a look at the CloudWatch logs.

The data is stored back to S3 as a CSV in the "write" prefix. The number of partitions equals number of output files.
The data is stored back to S3 as a CSV in the “write” prefix. The number of partitions equals the number of the output files.

Speeding up Spark development with Glue dev endpoint

Developing Glue transformation scripts is slow, if you just run a job after another. Provisioning the computation cluster takes minutes and you don’t want to wait after each change.

Glue has a dev endpoint functionality where you launch a temporary environment that is constantly available. For development and testing it’s both faster and cheaper.

Dev endpoint provides the processing power, but a notebook server is needed to write your code. Easiest way to get started is to create a new SageMaker notebook by clicking Notebooks under the Dev endpoint in the left panel.

About Glue performance

In the code example we did read the data first to Glue’s DynamicFrame and then converted that to native PySpark DataFrame. This method makes it possible to take advantage of Glue catalog but at the same time use native PySpark functions.

However, our team has noticed Glue performance to be extremely poor when converting from DynamicFrame to DataFrame. This applies especially when you have one large file instead of multiple smaller ones. If the execution time and data reading becomes the bottleneck, consider using native PySpark read function to fetch the data from S3.

Summary about the Glue tutorial with Python and Spark

Getting started with Glue jobs can take some time with all the menus and options. Hopefully this tutorial gave some idea what is the role of database, table, job and crawler.

The focus of this tutorial was in a single script, but Glue also provides tools to manage larger group of jobs. You can schedule jobs with triggers or orchestrate relationships between triggers, jobs and crawlers with workflows.

Learning the Glue console is one thing, but the actual logic lies in the Spark scripts. Tuning the code impacts significantly to the execution performance. That will be the topic of the next blog post.

AWS Glue works well for big data processing. This is a brief introduction to Glue including use cases, pricing and a detailed example.

Introduction to AWS Glue for big data ETL

AWS Glue works well for big data processing. This is a brief introduction to Glue including use cases, pricing and a detailed example.

AWS Glue is a serverless ETL tool in cloud. In brief ETL means extracting data from a source system, transforming it for analysis and other applications and then loading back to data warehouse for example.

In this blog post I will introduce the basic idea behind AWS Glue and present potential use cases.

The emphasis is in the big data processing. You can read more about Glue catalogs here and data catalogs in general here.

Why to use AWS Glue?

Replacing Hadoop. Hadoop can be expensive and a pain to configure. AWS Glue is simple. Some say that Glue is expensive, but it depends where you compare. Because of on demand pricing you only pay for what you use. This fact might make AWS Glue significantly cheaper than a fixed size on-premise Hadoop cluster.

AWS Lambda can not be used. A wise man said, use lambda functions in AWS whenever possible. Lambdas are simple, scalable and cost efficient. They can also be triggered by events. For big data lambda functions are not suitable because of the 3 GB memory limitation and 15 minute timeout. AWS Glue is specifically built to process large datasets.

Apply DataOps practices. Drag and drop ETL tools are easy for users, but from the DataOps perspective code based development is a superior approach. With AWS Glue both code and configuration can be stored in version control. The data development becomes similar to any other software development. For example the data transformation scripts written by scala or python are not limited to AWS cloud. Environment setup is easy to automate and parameterize when the code is scripted.

An example use case for AWS Glue

Now a practical example about how AWS Glue would work in practice.

A production machine in a factory produces multiple data files daily. Each file is a size of 10 GB. The server in the factory pushes the files to AWS S3 once a day.

The factory data is needed to predict machine breakdowns. For that, the raw data should be pre-processed for the data science team.

Lambda is not an option for the pre-processing because of the memory and timeout limitation. Glue seems to be reasonable option when work hours and costs are compared to alternative tools.

The simplest way of get started with the ETL process is to create a new Glue job and write code to the editor. The script can be either in scala or python programming language.

Extract. The script first reads all the files from the specified S3 bucket to a single data frame. You can think a data frame as a table in Excel. The reading can be just a one-liner.

Transform. This is the most of the code. Let’s say that the original data had 100 records per second. The data science team wants the data to be aggregated per each 1 minute with a specific logic. This could be just tens of code lines if the logic is simple.

Load. Write data back to another S3 bucket for the data science team. It’s possible that a single line of code will do.

The code runs on top of the spark framework which is configured automatically in Glue. Thanks to spark, data will be divided to small chunks and processed in parallel on multiple machines simultaneously.

What makes AWS Glue serverless?

Serverless means you don’t have machines to configure. AWS provisions and allocates the resources automatically.

The processing power is adjusted by the number of data processing units (DPU). You can do additional configuration, but it’s likely that a proof of concept works out of the box.

In an on-premise environment you would have to make a decision about the computation cluster size. A big cluster is expensive but fast. A small cluster would be cheaper but slow to run.

With AWS Glue your bill is the result the following equation:

[ETL job price] = [Processing time] * [Number of DPUs]

 

The on demand pricing means that the increase in processing power does not compromise with the price of the ETL job. At least in theory, as too many DPUs might cause overhead in processing time.

When is AWS Glue a wrong choice?

This is not an advertisement, so let’s give some critique for Glue as well.

Lots of small ETL jobs. Glue has a minimum billing of 10 minutes and 2 DPUs. With the price of 0.44$ per DPU hour, the minimum cost for a run becomes around 0.15$. The starting price makes Glue unappealing alternative to process small amount of data often.

Specific networking requirements. If you spin up a standard EC2 virtual machine, an IP address will be attached to it. The serverless nature of Glue means you have to put more effort on network planning in some cases. One such scenario would be whitelisting a Glue job in a firewall to extract data from an external system.

Summary about AWS Glue

The most common argument against Glue is “It’s expensive”. True, in a sense that the first few test runs can already cost a few dollars. In a nutshell, Glue is cost efficient for infrequent big data workloads.

In the big picture AWS Glue saves a lot of time and unnecessary hardware engineering. The costs should be compared against alternative options such as on-premise Hadoop cluster or development hours required for a custom solution.

As Glue pricing model is predictable, the business cases are straightforward to calculate. It might be enough to test just the critical parts of the ETL pipeline to become confident about the performance and costs.

I feel that optimizing the code for distributed computing has been more of a challenge than the Glue service itself. The next blog post will focus on how data developers get started with Glue using python and spark.