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Ephemeral Hadoop clusters using Google Compute Platform


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Spydra (Beta / Inactive)


Note This project is inactive.

Ephemeral Hadoop clusters using Google Compute Platform


Spydra is "Hadoop Cluster as a Service" implemented as a library utilizing Google Cloud Dataproc and Google Cloud Storage. The intention of Spydra is to enable the use of ephemeral Hadoop clusters while hiding the complexity of cluster lifecycle management and keeping troubleshooting simple. Spydra is designed to be integrated as a hadoop jar replacement.

Spydra is part of Spotify's effort to migrate its data infrastructure to Google Compute Platform and is being used in production. The principles and the design of Spydra are based on our experiences in scaling and maintaining our Hadoop cluster to over 2500 nodes and over 100 PBs of capacity running about 20,000 independent jobs per day.

Spydra supports submitting data processing jobs to Dataproc as well as to existing on-premise Hadoop infrastructure and is designed to ease the migration to and/or dual use of Google Cloud Platform and on-premise infrastructure.

Spydra is designed to be very configurable and allows the usage of all job types and configurations supported by the gcloud dataproc clusters create and gcloud dataproc jobs submit commands.

Development Status

Spydra is the rewrite of a concept that has been developed at Spotify for more than a year. The current version of Spydra is in beta, used in production at Spotify, and actively developed and supported by our data infrastructure team.

Spydra is in beta and things might change but we are aiming at not breaking the currently exposed APIs and configuration.

Spydra at Spotify

At Spotify, Spydra is being used for our on-going migration to Google Cloud Platform. It handles the submission of on-premise Hadoop jobs as well as Dataproc jobs, simplifying the switch from on-premise Hadoop to Dataproc.

Spydra is packaged in a docker image that is used to deploy data pipelines. This docker image includes Hadoop tools and configurations to be able to submit to our on-premise Hadoop cluster as well as an installation of gcloud and other basic dependencies required to execute Hadoop jobs in our environment. Pipelines are then scheduled using Styx and orchestrated by Luigi which then invokes Spydra instead of hadoop jar.


Spydra is built as a wrapper around Google Cloud Dataproc and designed not to have any central component. It exposes all functionality supported by Dataproc via its own configuration while adding some defaults. Spydra manages clusters and submits jobs invoking the gcloud dataproc command. Spydra ensures that clusters are eventually deleted by updating a heartbeat marker in the cluster's metadata and utilizes initialization-actions to set up a self-deletion script on the cluster to handle the deletion of the cluster in the event of client failures.

For submitting jobs to an existing on-premise Hadoop infrastructure, Spydra utilizes the hadoop jar command which is required to be installed and configured in the environment.

For Dataproc as well as on-premise submissions, Spydra will act similar to hadoop jar and print out driver output.


Spydra is designed to ease the usage of Google Compute Platform credentials by utilizing service accounts. The same credential that is used locally by Spydra to manage the cluster and submit jobs, is also by default forwarded to the Hadoop cluster when calling Dataproc. This means that access rights to resources need only be given to a single set of credentials.

Storing Execution Data and Logs

To make job execution data available after an ephemeral cluster was shut down, and to provide similar functionality to the Hadoop MapReduce History Server, Spydra stores execution data and logs on Google Cloud Storage, grouping it by a user-defined client id. Typically client id is unique per job. The execution data and logs are then made available via Spydra commands. These allow spinning up a local MapReduce History Server to access execution data and logs as well as dumping them.


Spydra has an experimental autoscaler which can be executed on the cluster. It monitors the current resource utilization on the cluster and scales the cluster according to a user defined utilization factor and maximum worker count by adding preemptible VMs. Note that the use of preemptible VMs might negatively impact performance as nodes might be shut down any time.

The autoscaler is being installed on the cluster using a Dataproc initialization-action.

Cluster Pooling

Spydra has experimental support for cluster pooling withing a single Google Compute Platform project. Cluster pooling can be used to limit the resources used by the job submissions, and also limit the cluster initialization overhead. The maximum number of clusters to be used can be defined as well as their maximum lifetime. Upon job submission, a random cluster is chosen to submit the job into. When reaching their maximum lifetime, pooled clusters are being deleted by the self-deletion mechanism.



There's a pre-built Spydra on maven central. This is built using the parameters from .travis.yml, the bucket spydra-init-actions is provided for by Spotify.


To be able to use Dataproc and on-premise Hadoop, a few things need to be set up before using Spydra.

Spydra CLI

Spydra CLI supports multiple sub-commands:


$ java -jar spydra/target/spydra-VERSION-jar-with-dependencies.jar submit --help

usage: submit [options] [jobArgs]
    --clientid <arg>     client id, used as identifier in job history output
    --spydra-json <arg>  path to the spydra configuration json
    --jar <arg>          main jar path, overwrites the configured one if
    --jars <arg>         jar files to be shipped with the job, can occur
                         multiple times, overwrites the configured ones if
    --job-name <arg>     job name, used as dataproc job id
 -n,--dry-run            Do a dry run without executing anything

Only a few basic things can be supplied on the command line; a client-id (an arbitrary identifier of the client running Spydra), the main and additional JAR files for the job, and arguments for the job. For any use-case requiring more details, the user needs to create a JSON file and supply the path to that as a parameter. All the command-line options will override the corresponding options in the JSON config. Apart from all the command-line options and some general settings, it can also transparently pass along parameters to the gcloud command for cluster creation or job submission.

A job name can also be supplied. This will be sanitized and have a unique identifier attached to it, which will then be used as the Dataproc job ID. This is useful in finding the job in the Google Cloud Console.

The spydra-json argument

All properties that cannot be controlled via the few arguments of the submit command, can be set in the configuration file supplied with the --spydra-json parameter. The configuration file follows the structure of the cloud dataproc clusters create and cloud dataproc jubs submit commands and allows to set all the possible arguments for these commands. The basic structure looks as follows:

  "client_id": "spydra-test",                 # Spydra client id. Usually left out as set by the frameworks during runtime.
  "cluster_type": "dataproc",                 # Where to execute. Either dataproc or onpremise. Defaults to onpremise.
  "job_type": "hadoop",                       # Defaults to hadoop. For supported types see gcloud dataproc jobs submit --help
  "log_bucket": "spydra-test-logs",           # The bucket where Hadoop logs and history information are stored.
  "region": "europe-west1",                   # The region in which the cluster is spun up
  "cluster": {                                # All cluster related configuration
    "options": {                              # Map supporting all options from the gcloud dataproc clusters create command
      "project": "spydra-test",
      "num-workers": "13",
      "worker-machine-type": "n1-standard-2", # The default machine type used by Dataproc is n1-standard-8.
      "master-machine-type": "n1-standard-4"
  "submit": {                                 # All configuration related to job submission
    "job_args": [                             # Job arguments. Usually left out as set by the frameworks during runtime.
    "options": {                              # Map supporting all options from the gcloud dataproc jobs submit [hadoop,spark,hive...] command
      "jar": "/path/my.jar"                   # Path of the job jar file. Usually left out as set by the frameworks during runtime.

For details on the format of the JSON file see this schema and these examples.

Minimal Submission Example

Using only the command-line:

$ java -jar spydra/target/spydra-VERSION-jar-with-dependencies.jar submit --client-id simple-spydra-test --jar hadoop-mapreduce-examples.jar pi 8 100

JSON config:

$ cat examples.json
  "client_id": "simple-spydra-test",
  "cluster_type": "dataproc",
  "log_bucket": "spydra-test-logs",
  "region": "europe-west1",
  "cluster": {
    "options": {
      "project": "spydra-test"
  "submit": {
    "job_args": [
    "options": {
      "jar": "hadoop-mapreduce-examples.jar"
$ spydra submit --spydra-json example.json
Cluster Autoscaling (Experimental)

The Spydra autoscaler provides automatic sizing for Spydra clusters by adding enough preemptible worker nodes until a user supplied percentage of containers is running in parallel on the cluster. It enables cluster sizes to automatically adjust to growing resource needs over time and removes the need to come up with a good size when scheduling a job executed on Spydra. The autoscaler has two modes, upscale only and downscale.

Downscale will remove nodes when the cluster is not fully utilized. After choosing to downscale, it will wait for the downscale_timeout to allow active jobs to complete before terminating nodes. Note that though nodes may not have active YARN containers running, active jobs may be storing intermediate "shuffle" data on them. See Dataproc Graceful Downscale for more information.

To enable autoscaling, add an autoscaler section similar to the one below to your Spydra configuration.

  "cluster:" {...},
  "submit:" {...},
  "auto_scaler": {
    "interval": "2",        # Execution interval of the autoscaler in minutes
    "max": "20",            # Maximum number of workers
    "factor": "0.3",        # Percentage of YARN containers that should be running at any point in time 0.0 to 1.0.
    "downscale": "false",    # Whether or not to downscale.
    # If downscale is enabled, how long in minutes to wait for active jobs to finish
    # before terminating nodes and potentially interrupting those jobs.
    # Note that the autoscaler will not be able to add nodes during this interval.
    "downscale_timeout": "10"
Static Cluster Submission

If you prefer to manage your Dataproc clusters manually you still can use Spydra for job submission and just skip dynamic cluster creation part. The only change that is needed to be done to Spydra configurations is that you need to specify the name of the cluster you want to submit the job to. Here is an example:

  "client_id": "simple-spydra-test",
  "cluster_type": "dataproc",
  "log_bucket": "spydra-test-logs",
  "submit": {
    "options": {
        "project": "spydra-test",
        "cluster": "NAME_OF_YOUR_CLUSTER"
    "job_args": [
    "options": {
      "jar": "hadoop-mapreduce-examples.jar"

Also notice that project parameter is specified in submit/options section instead of cluster/options section.

Cluster Pooling (Experimental)

Disclaimer: The usage of the pooling is experimental!

The Spydra cluster pooling provides automatic pooling for Spydra clusters by selecting an existing cluster according to certain conditions.

To enable cluster pooling add a pooling section similar to the one below to your Spydra configuration.

  "cluster:" {...},
  "submit:" {...},
  "pooling": {
    "limit": 2,     # limit of concurrent clusters
    "max_age": "P1D"# A java.time.Duration for the maximum age of a cluster
Submission Gotchas
  • You can use -- if you need to pass a parameter starting with dashes to your job, e.g. submit --jar=jar ... -- -myParam
  • Don't forget to specify = for arguments like --jar=$jar, otherwise the CLI parsing will break.
  • If the specified jar contains a Main-Class entry in it's manifest, specifying --mainclass will often lead to undesired behaviour, as the value of main-class will be passed as an argument to the application instead of invoking this class.
  • Not setting the default fs to GCS using the fs.defaultFS property can lead to crashes and undesired behavior as a lot of the frameworks use the default filesystem implementation instead of getting the correct filesystem for a given URI. It can also lead to the Crunch output committer working very slowly while copying all files from HDFS to GCS in a last non-distributed step.

Running an Embedded JobHistoryServer

The run-jhs is designed for an interactive exploration of the job execution. This command spawns an embedded JobHistoryServer that can display all jobs executed using the client id associated with your job submission. Familiarity with the use of JobHistoryServer from on-premise Hadoop is assumed. The JHS is accessible on default port 19888.

The client id used when executing the job, and the log bucket is required for running run-jhs command.

java -jar spydra/target/spydra-VERSION-jar-with-dependencies.jar run-jhs --clientid=JOB_CLIENT_ID --log-bucket=LOG_BUCKET

Retrieving Logs

The dump-logs command will dump logs for an application to stdout. Currently only full logs of the YARN application can be dumped - similarly to YARN logs when no specific container is specified. This is useful for processing/exploration with further tools in the shell.

The client id used when executing the job, the Hadoop application id, and the log bucket is required for running dump-logs command.

java -jar spydra/target/spydra-VERSION-jar-with-dependencies.jar dump-logs --clientid=MY_CLIENT_ID --username=HADOOP_USER_NAME --log-bucket=LOG_BUCKET --application=APPLICATION_ID

Retrieving History Data

The history files can be dumped as in regular Hadoop using the dump-history command.

The client id used when executing the job, the Hadoop application id, and the log bucket is required for running dump-history command.

java -jar spydra/target/spydra-VERSION-jar-with-dependencies.jar dump-history --clientid=MY_CLIENT_ID --log-bucket=LOG_BUCKET --application=APPLICATION_ID

Accessing Hadoop Web Interfaces for Ephemeral Clusters

Dataprocxy can be used to open the web interfaces of the Hadoop daemons of an ephemeral cluster as long as the cluster is running.



  • Java JDK 8
  • Maven 3.2.2
  • A Google Compute Platform project with Dataproc enabled
  • A Google Cloud Storage bucket for uploading init-actions. Ensure that this bucket is readable with all credentials used with Spydra.
  • A Google Cloud Storage bucket for storing integration test logs
  • JSON key for a service account with editor access to the project and bucket.
  • The environment variable GOOGLE_APPLICATION_CREDENTIALS pointing at the location of the service account JSON key
  • gcloud authenticated with the service account
  • gsutil authenticated with the service account

Integration Test Configuration

In order to run integration tests, basic configuration needs to be provided during the build process. Create a file with name integration-test-config.json similar to the one below and reference it during the maven invocation.

  "log_bucket": "YOUR_GCS_LOG_BUCKET",
  "region": "europe-west1"

Replace the YOUR_GCS_LOG_BUCKET with a bucket you have in your GCP project for storing the logs.

The project will be taken from the service account credentials, you do not need to specify the project parameter in integration-test-config.json (or elsewhere).

Notice that the file name must be exactly integration-test-config.json as that is what the integration test will search for when it is run on the maven verify phase.

Integration testing with application default credentials

Due to a limitation in the GCS Connector library, the integration tests do not work when using application default credentials, unless the tests are launched on a Google Compute Platform managed node. Scripts for launching the tests in a Google Kubernetes Engine cluster have been provided in integration_test_k8s

Build, Test and Package

In the following command, replace YOUR_INIT_ACTION_BUCKET with the bucket you created when setting up the prerequisites and YOUR_TEST_CONFIG_DIR with a directory name containing the file integration-test-config.json you created in the previous step. YOUR_TEST_CONFIG_DIR cannot be the same as the package root, so create a separate directory for this purpose. Then execute the maven command:

mvn clean install -Dinit-action-uri=gs://YOUR_INIT_ACTION_BUCKET/spydra -Dtest-configuration-dir=YOUR_TEST_CONFIG_DIR

Executing the maven command above will run the integration tests, and create a spydra-VERSION-jar-with-dependencies.jar under spydra/target that packages Spydra, which can be executed with java -jar. Using package instead of install can be used to run just unit-tests and package Spydra.

If you want to copy the init-scripts into the defined init-action bucket, activate profile install-init-scripts:

mvn clean install -Pinstall-init-scripts -Dinit-action-uri=gs://YOUR_INIT_ACTION_BUCKET/spydra -Dtest-configuration-dir=YOUR_TEST_CONFIG_DIR

Do not run Maven deploy step, as it will try to upload created packages into the Spotify owned repositories, which will fail unless you have Spotify specific credentials.


If you use Spydra and experience any issues, please create an issue under this Github project in here.

You can also ask for help and talk to us on Spydra related issues in Spotify FOSS Slack on channel #spydra.


This project adheres to the Open Code of Conduct. By participating, you are expected to honor this code.