Spark connection & management

Architecture overview

Apache Spark is the recommended federation engine used by Lyftron. The are two primary models of working with Spark:

  • connecting to an existing standalone cluster (e.g. Azure HDInsight),
  • hosting a local instance managed by Lyftron,

The basic key concept here is the Driver which is a small Java application acting as a proxy for delegating incoming requests to the Spark execution engine.

The driver is bundled in a JAR file and then submitted to the cluster using spark-submit.

Once started, the driver starts an in-process Thrift/Http server on the given port and starts listening for incoming connections from Lyftron.

Incoming requests are authenticated using an API-key. Additionally, the driver opens reverse connections to Lyftron via TDS which on the other hand is authenticated using a username/password pair configured on the Spark connection level.

The important thing to note here is that only a few basic parameters (e.g. driver memory) must be passed as spark-submit arguments. Most parameters are configurable on the connection level and will be sent over-the-wire during the first data request.

The driver starts in a "NotInitialized" state which means it doesn't try to connect to Spark until the first request is done.

The actual details differ depending on the connection model used.

Local Spark instance


In this model, Spark is started as a child (although independent) JVM process of Lyftron main process. This is achieved via programmatic invoke of spark-submit together with passing the driver JAR file as the primary argument. Other arguments, such as port number are passed as command line arguments as well. At this point, a new Java application is started but Spark itself is not initialized yet. For this to happen the first connection must be established so the Spark parameters can be passed along.

Connecting to Spark

For general information regarding connections see Connections.

Lyftron comes with a single pre-configured local Spark connection.

The important thing to note here is that Spark/Hive configuration is set on different levels. Except for standard environment parameters, a small bit of configuration is stored in config files (see: Advanced configuration) and most on the connection level. At runtime, the configurations are merged in this exact order, which means if a given parameter is defined in a configuration file and then defined again on the connection level, the former one will be used. This behavior is guaranteed.

When adding a new connection the following parameters apply:

Parameter Description Default value
Name A human-friendly name of the connection e.g. Local Spark 2.1
Host:Port A CSV list of server:port pairs to connect to, see: Cluster failover. For local instance use localhost:port localhost:8400
Protocol The protocol that the driver will use to exchange data with Lyftron. Possible values: Thrift, http Thrift
Spark dialect (= protocol version). The dialect version used to communicate with the driver. Possible values: 1.6, 2.0
Driver API key The security key used to authenticate requests incoming from Lyftron. Using the default value is highly NOT recommended. lyftron-key
Username The account to be used for authenticating reverse connections from the driver
Password The password for the account above
Default database The database used for storing data for cached views
Reverse connection host address The address under which Lyftron can be reached by the driver. For local instance use localhost
Node connection timeout The timeout used for waiting for each cluster node to respond. See: Cluster failover 10s
Cluster initialization timeout The timeout given for the instance to initialize when doing the initial request 30s
Batch size Chunk size used when retrieving query results from Spark 16777216 bytes
Target partition size See: Partitioning strategy 128000000 bytes
Max partitions for auto repartitioning See: Partitioning strategy 256

Scheduler pools

To learn about Spark scheduler pools in general, please consult

In short, scheduler pools describe relative part of cluster's resources that will be assigned to process given task. Lyftron's spark connections support 6 scheduler pools:

  • default - scheduler pool used in case no scheduler pool configuration is provided
  • ETL - scheduler pool used by cache load tasks, executes job in FIFO order
  • S, M, L, XL - 4 pools that user can specify for their queries with ascending allocation of resources

To specify pool to be used for given query follow your query with option clause. For example:




Exact configuration values for each scheduler pool can be reviewed and configured on Spark connection screen: Spark scheduler pools

The meaning of the pool properties is a follows:

  • Mode: FIFO means tasks are queued one after the other. FAIR means that tasks can run in parallel and share resources.
  • Weight: Decides how much of cluster's resources will this pool get relative to other pools.
  • Min share: Share of cluster's resources assigned by FAIR scheduler (if possible) before allocation of rest of resources takes place.

You can review pools used by spark for each query in Spark's admin portal, just go to tab 'Stages': Spark stages

Spark variables

Any properties prefixed with spark can be defined in this section and will be used when creating a new Spark context. Several of them have been predefined by the Lyftron team with the most noticeable being:

Parameter Description Default value
spark.ui.port The Spark UI port. 4040
spark.local.dir The local working directory of Spark. C:\ProgramData\Lyftron\tmp

Hive configuration

Under the hood, Spark uses Hive as a query execution engine. One of the important properties here is the warehouse directory i.e. a file system folder used as physical data storage.

Depending on version of Spark used, this parameter is set by either/or hive.metastore.warehouse.dir and spark.sql.warehouse.dir. These values are set in the config files (see: Advanced configuration) but can be passed on the connection level as well.

We highly recommend to always consult the documentation of Spark to make sure the right values are set. Excerpt from the Apache Spark v2.2 documentation:

> Note that the hive.metastore.warehouse.dir property in hive-site.xml is deprecated since Spark 2.0.0. 
> Instead, use spark.sql.warehouse.dir to specify the default location of the database in a warehouse. 
> You may need to grant write privilege to the user who starts the Spark application.

For the local instance the following parameters are also important:

Parameter Description Default value
javax.jdo.option.ConnectionURL JDBC connection string to the Hive metastore jdbc:sqlserver://localhost;databaseName=SparkMetastore;create=true;
javax.jdo.option.ConnectionUserName JDBC login to the Hive metastore
javax.jdo.option.ConnectionPassword JDBC password to the Hive metastore
hive.exec.scratchdir C:/ProgramData/Lyftron/tmp/hive Hive tmp folder

Connection test and Spark validation

Once configured the Test Connection button can be used to verify whether the instance is responsive. Note, that this only test if the given driver instance responds to incoming connections. To fully test the configuration, the following steps are one of the methods to trigger and check Spark initialization:

  • Using the Lyftron Databases menu create a new database named "spark-test-db" linked to the newly created connection (for more information related to database creation see: Databases)

Create Spark db

  • On the newly created database add new view of type Custom SQL named "test_view":

Create Spark view

  • On the next screen type select 1 as col; and click Parse

Create Spark view

  • Finish creating the view
  • Click the view on the list and go to Caching and Partitioning, click Edit
  • Set Caching mode to Persistent caching - one table and Save

Create Spark view

  • Go to Management tasks and run Rebuild persistent cache task

Create Spark view

  • Go to query editor and run the following query on the Spark database:
select [col] 
from [spark-test-db].[dbo].[test_view]     
union all
select [col] 
from [spark-test-db].[dbo].[test_view]
  • The result should look more or less like this:

Create Spark view

These actions should force Spark to initialized. To verify it change the URL in the browser from e.g. http://lyftron-example:9000 to http://lyftron-example:4040 where 4040 is the Spark GUI port set in spark.ui.port variable (4040 is the default value).

Spark GUI should open and you should see the query executed:

Local Spark

You can click on the tabs to see different jobs and parameters which might be useful for future debugging and optimization.

Partitioning strategy

When JOIN queries are pushed-down to execute on Spark, the data from the original database has to be copied to Spark. Lyftron registers external data sources as JDBC data frames, optionally applying repartition(n) function invocation. If the data frame is too large it has to be broken into smaller chunks. The Target partition size (bytes) parameters defines the target size of these chunks. The default value 128 MiB is recommended as a starting point.

The second parameter Max partitions for auto repartitioning (default: 256) defines the upper limit for the number of partitions to be created.

This means the theoretical limit for a single data transfer, in this case, would be 128 MB * 256 =~ 32 GB which is far more than reasonable for an ad-hoc query. In such cases, it's recommended to create cached-view and bulk load the data.

Local Instance Configuration

Local instance management can be found under the Administer -> Local Spark Instances menu

Main menu

Lyftron is shipped with a single pre-configured instance of Spark.

The Refresh button runs a state check to ensure the State column holds the latest information. The view has not auto-refreshing functionality so it might be necessary to use it a few times during state transition.

The Add button allows configuring a new local instance.

Clicking on a given row brings up the details blade with all parameters shown in read-only mode.

Details menu

Refresh button works the same way as in the previous screen.

Start, Stop and Kill buttons are used for controlling the instance state.

The difference between Stop and Kill is that the former sends a remote "self-shutdown" command allowing the instance to free all used resources and close itself softly. This process may take up to several minutes. Kill, on the other hand, sends an OS-level kill signal which should terminate the underlying JVM process (not recommended unless necessary).

You can run as many instances as you want at the same time as long as they use different ports.


The following values can be configured for a given instance:

Parameter Description Default value
Name A human-friendly name of the instance e.g. Spark 2.1
Port The port that the driver will open to communicate with Lyftron. 8400
Protocol The protocol that the driver will use to exchange data with Lyftron. Possible values: Thrift, http Thrift
Spark dialect (= protocol version). The dialect version used to communicate with the driver. Possible values: 1.6, 2.0
Driver API key The security key used to authenticate incoming requests - this must match the key given when configuring the connection to Spark. Using the default value is highly NOT recommended. lyftron-key
Driver version The version of the driver to be deployed to the instance - should match the used spark version. 2.1.0
SPARK_HOME The root directory of the used Spark distribution. %PROGRAMDATA%\Lyftron\lib\spark\spark-2.1.0-bin-hadoop2.6
SPARK_CONF_DIR The directory where Spark configuration is being stored %PROGRAMDATA%\Lyftron\conf\spark
Log directory The directory where Spark log files are being generated. No spaces allowed. See also: Disable Log Tracing %PROGRAMDATA%\Lyftron\logs
Spark master Spark master URL local[*]
Driver memory Amount of memory to use for the driver process, i.e. where SparkContext is initialized. 8g
Executor memory Amount of memory to use per executor process 8g
Total executor cores The number of cores to use on each executor 4
_JAVA_OPTIONS Value of _JAVA_OPTIONS variable picked up by Spark. Note: setting only "-Xms1G" might trigger the following startup error: "'Initial' is not recognized as an internal or external command"
Autostart Sets whether the given instance should be started together with Lyftron. False
Persistent Sets whether the given instance should be left alive even the master Lyftron process is stopped. False
Enable Log Tracing Checking this flag will cause Lyftron to start tracing and copying Spark log files to Lyftron. It's useful when using a logging engine such as SEQ to have all logs in one place. False

Advanced configuration

Additionally Lyftron stores a few configuration files in C:\ProgramData\Lyftron\conf\spark (default).

File Description
core-site.xml Lyftron-specific HDFS configuration
hive-site.xml Lyftron-specific Hive configuration
lyftron-site.xml Lyftron configuration parameters
spark-env.cmd Spark environment variables configuration

Standalone cluster


For more resource consuming operations it makes sense to use a preconfigured standalone cluster. Once setup you should configure a new Local instance except for the Spark master parameter should be set to the proper URL e.g. spark://HOST:PORT.



If you don't have a running Spark cluster already in place then Microsoft HDInsight might be the solution to quickly get one running.

Lyftron fully supports HDInsight as the data federation engine.

HDInsight configuration

To add a new cluster, you may use the Azure Portal:

Azure menu

The following table summarizes the important parameters to be defined:

Parameter Description
Cluster login username Login that will be used to access Ambari
Cluster login password Password that will be used to access Ambari
Secure Shell (SSH) username See: Troubleshooting
Cluster type Use "Spark"
Version Spark version - Lyftron supports Spark from 1.6.2 upwards

It is important to note that the communication between HDI and Lyftron is required both ways i.e. Lyftron must be able to reach the driver on the lyftron.driver.port value (default: 8400) and the driver must be able to reach Lyftron on the TDS port (default: 1200). To make it work, the HDI cluster should be placed a proper VLAN meeting these requirements.

Azure menu

Driver deployment

(further instructions assume that the HDInsight cluster is already configured and running).

The basic idea is similar to Spark Local instance, except the driver is deployed using Azure Portal on all (two) HDInsight head nodes (with failover enabled by default) and submitted with Spark master = master yarn which means that instead of running a new instance we are attaching to the running YARN process.

Building the tarball

Uploading the driver is done using the HDInsight Script Action mechanism. To do this the driver must be packed in a .tar.gz file and uploaded to an arbitrary publicly visible Azure blob.

The necessary tools and folder structure can be found in the %LYFTRON_INSTALL_FOLDER%\HDInsight\version directory. We recommend copying this folder a local-user location to avoid problems with Windows elevated privileges.

The important step to be done at this point it configuring the primary driver parameters. Open lyftron\lyftron-site.xml with any text editor. The following values are particularly important:

Parameter Description
lyftron.api.key must match the value entered later when creating a connection to the cluster; shouldn't be left with the default value
lyftron.driver.port The port the driver will listen on (default: 8400)
lyftron.driver.protocol The connection protocol - possible values: thrift, http
lyftron.ha.enable See: Cluster failover

Additionally a second file contains the Log4J configuration for the Lyftron driver, which can be tweaked as well. By default, it logs all data into ./logs directory separately for each HDInsight head node with TRACE level. Contrary to the local instance, the logs are not streamed back to the master Lyftron instance in any way and thus the only way to access them is logging directly to the given node e.g. by SSH (see: Troubleshooting).

Once done, the Build-Tarball.cmd scripts use 7Zip to the tarball and gzip all necessary files. The following line should be adjusted if necessary: set ZIP_EXE="C:\Program Files\7-Zip\7z.exe"

If everything went fine, a new file like lyftron21.tar.gz should appear. Now this file together with must be uploaded to a public Azure blob. You can either do it manually using a tool such as Microsoft Azure Storage Explorer or use the provided Build-Tarball-and-Upload.ps1 which runs the Build-Tarball.cmd and then uses the Azure Powershell to upload both files (installation required).

At this point you either have to use an existing or create a new Azure Storage Account using Azure Portal:

Azure menu

Once created, find the new resource to obtain the StorageAccountName and StorageAccountKey:

Azure menu

Edit the Build-Tarball-and-Upload.ps1 and copy-paste the values to the correct lines of the file. $ContainerName can be changed as well. Once run (using Windows Powershell ISE) a progress bar might appear and after several second the outcome should be more or less like the following:

Azure Powershell

The two highlighted URLs are the final locations of the files. You can copy-paste them in a web browser to make sure they are publicly available (they can be removed after the deployment process).

Running the installation script

On the HDI cluster management screen please find the Script actions button.

Azure menu

Then click Submit new

Script action

You can see the previous submissions as well - the Resubmit option makes it quite convenient to run the same script again in case you need to e.g. change the parameters.

The following table summarizes the inputs

Parameter Description
Name A user-friendly name of the script e.g. qdriver202-build-1445
Bash script URI URL pointing the the script generated above e.g.
Head Should the script run on head nodes? Check
Worker Should the script run on worker nodes? Uncheck
Zookeeper Should the script run on zookeeper nodes? Uncheck
Parameters Script arguments - see below
Persist this script action This only applies to worker nodes (Uncheck)

Script arguments should be passed as follows: - the URL of the .tar.gz generated above - spark-submit arguments passed in quotes

So the sample field of this value could be "--driver-memory 4g --executor-memory 4g --executor-cores 1"

Clicking Create should submit the script for execution. Usually, it takes a few minutes for the script to execute. Failures usually occur faster.

Successful execution doesn't always mean everything went fine.. Go through the next chapter to verify.

Using Ambari

One of the properties on the cluster Dashboard is the URL e.g. Clicking this URL opens Ambari - a graphical environment used for monitoring the cluster.

Apart from many different functions, you can also monitor the execution of submitted Script actions. They can be accessed during and after execution by clicking in the upper right menu:


This brings up the Background operations window


Drilling down will allow to access the execution logs for every requested cluster node (both head nodes in this case):


If everything went fine the logs for every head node should look more or less like the following:


--2017-04-25 09:08:37--
Resolving (
Connecting to (||:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 5602881 (5.3M) [application/octet-stream]
Saving to: /tmp/lyftron/lyftron21.tar.gz
0K .......... .......... .......... .......... ..........  0% 1.11M 5s
50K .......... .......... .......... .......... ..........  1%  144M 2s
100K .......... .......... .......... .......... ..........  2% 2.19M 2s
5300K .......... .......... .......... .......... .......... 97%  178M 0s
5350K .......... .......... .......... .......... .......... 98%  110M 0s
5400K .......... .......... .......... .......... .......... 99%  169M 0s
5450K .......... .......... .                               100%  250M=0.3s
2017-04-25 09:08:37 (17.2 MB/s) - /tmp/lyftron/lyftron21.tar.gz saved [5602881/5602881]


Starting Lyftron install script on, OS VERSION: 16.04
Removing Lyftron installation and tmp folder
Downloading Lyftron tar file
Unzipping Lyftron
Making Lyftron a service and starting
Using systemd configuration
('Start downloading script locally: ', u'')
Fromdos line ending conversion successful
Custom script executed successfully
Removing temp location of the script
Command completed successfully!

The last thing we need to note here is the head node IP addresses we will need them in a minute to connect to the driver. These can be found in the Hosts tab in Ambari:


At this point, we can try to add a new Spark connection.

Connecting to HDInsight

For general information see: Connecting to Local Spark

The recommended way to start is to the use the predefined connection template called azure_spark_conn_template

The following table summarizes the differences in connection parameter semantics compared to local instance connection:

Parameter Description Default value
Host:Port CSV-list all local head-node IPs here e.g., localhost:8400
Protocol Must match the one defined in lyftron-site.xml Thrift
Spark dialect It depends on the used HDI version. 2.0 is recommended for new clusters.
Driver API key Must match the one defined in lyftron-site.xml lyftron-key
Reverse connection host address An IP address of Lyftron instance that the driver will use for reverse connections. Must be reachable from both head nodes.
Node connection timeout The timeout used for waiting for each cluster node to respond. See: Cluster failover 10s
Cluster initialization timeout Since HDI cluster make take up to a few minutes to boot it's recommended to increase the default value e.g. to 180s 30s

Scheduler pools

Same as Local instance

Spark variables

Any properties regarding file system folders shouldn't be set to avoid errors. spark.ui.port is not used - read below.

Hive configuration

Any properties regarding file system folders shouldn't be set to avoid errors.

Connection test and Spark validation

Once configured, the same steps as for local instance can be done to ensure Spark was initialized. But since on HDInsight the driver is deployed a YARN client accessing the Spark GUI in done differently:

Under Ambari click Services -> Yarn -> Quick Links -> any of the nodes -> ResourceManager UI


This will open the YARN UI. Since there's only one instance per cluster, it doesn't matter which node you select.

The same UI is also available directly via URL like

If initialization was trigger our application should first appear on the SUBMITTED list and then move to RUNNING:


Clicking the ID shows the details screen and from there Tracking URL: ApplicationMaster link leads to the Spark UI:


Advanced management / troubleshooting

Using WinSCP to view the folder structure

All HDI nodes are Linux-based machines which enable easy management. As an example, we are using WinSCP. The following screenshot shows a sample connection configuration. The Host name field contains the IP address of the first head node.


Navigating to /usr/share/lyftron will take you to the installation folder:


  • the logs folder contains the driver log files,
  • lyftron-site.xml can be edited here as well, but service restart is required,
  • can be used to adjust the logging levels,

Note that we are on a single of two head nodes, so any action must be taken separately for each node. This applies to log files as well.

Using Putty to manage the service

Together with WinSCP, we recommend installing Putty. Then directly from WinSCP we can open a new Putty session and login to the node using CLI.

Starting from HDI 2.0 the driver is run a standard systemd deamon so we can use systemctl to manage the lyftron service.

e.g. running systemctl status lyftron should print something like:

lyftron.service - lyftron service
   Loaded: loaded (/etc/systemd/system/lyftron.service; enabled; vendor preset: enabled)
   Active: active (running) since Tue 2017-04-25 09:08:38 UTC; 3h 19min ago
 Main PID: 37703 (bash)
    Tasks: 20
   Memory: 112.8M
      CPU: 12.974s
   CGroup: /system.slice/lyftron.service
           ??????37703 bash /usr/share/lyftron/
           ??????37708 /usr/lib/jvm/java-8-openjdk-amd64/bin/java -Dhdp.version= -cp /usr/hdp/current/hive-server2/lib/sqljdbc41.jar:/usr/share/lyftron

Apr 25 09:08:44 hn0-hdi201[37703]:   Starting conductor
Apr 25 09:08:44 hn0-hdi201[37703]: 17/04/25 09:08:44 INFO AppConductor: HA is enabled, attempting to start failover service and join leader ele
Apr 25 09:08:44 hn0-hdi201[37703]:   17/04/25 09:08:44 INFO AppConductor: Changing NodeState state to: WaitingLeaderResult
Apr 25 09:08:44 hn0-hdi201[37703]:   17/04/25 09:08:44 INFO FailoverService: Starting failover monitoring - path: /lyftron-leader-election time
Apr 25 09:08:44 hn0-hdi201[37703]:   17/04/25 09:08:44 INFO ActiveStandbyElector: Session connected.
Apr 25 09:08:44 hn0-hdi201[37703]:   17/04/25 09:08:44 INFO ActiveStandbyElector: Successfully created /lyftron-leader-election in ZK.
Apr 25 09:08:44 hn0-hdi201[37703]:   17/04/25 09:08:44 INFO FailoverService: Joining election, my data:
Apr 25 09:08:44 hn0-hdi201[37703]:   17/04/25 09:08:44 INFO LyftronServer: starting thrift server on port 8400
Apr 25 09:08:44 hn0-hdi201[37703]:   17/04/25 09:08:44 INFO AppConductor: Releasing Spark
Apr 25 09:08:44 hn0-hdi201[37703]:   17/04/25 09:08:44 INFO AppConductor: Changing NodeState state to: Standby

sudo systemctl restart lyftron can be used in case of serious problems.

Cluster failover

Lyftron driver supports automatic failover using Apache Zookeeper.

To make it work the driver has to have at least two instances running and the Zookeeper service must be available. There are two parameters in the lyftron-site.xml regarding this:

Parameter Description
lyftron.ha.enable Enables/disables failover. Enabled by default in the HDInsight, disabled for local instances. Possible values: true, false
ha.zookeeper.quorum A CSV-list of a server:port values of Zookeeper servers. In HDInsight the value is already provided by the environment and is not required.

Once started, all driver instances will participate in a leader election and choose the active node. All other nodes will continue to listen for incoming connection but will transition to StandBy state.

Now when adding a new Spark connection, all servers should be entered in the Host:Port configuration field. Lyftron will try to find the active node using round-robin. Node connection timeout determines the wait time per each node. If an active node is not found within the Cluster initialization timeout the connection will fail.

If for any reason one of the nodes fails, a new election will start automatically and the new active node is chosen. Any queries that were halted by the node failure, will be rerun automatically once a new active node is found.