memsql / memsql-spark-connector

A connector for MemSQL and Spark

GitHub

MemSQL Spark Connector

Version: 3.0.4 Continuous Integration License

Getting Started

You can find the latest version of the connector on Maven Central and spark-packages.org. The group is com.memsql and the artifact is memsql-spark-connector_2.11.

You can add the connector to your Spark application using: spark-shell, pyspark, or spark-submit

$SPARK_HOME/bin/spark-shell --packages com.memsql:memsql-spark-connector_2.11:3.0.4-spark-2.4.4

We release two versions of the memsql-spark-connector, one per Spark version. An example version number is: 3.0.4-spark-2.3.4 which is the 3.0.4 version of the connector, compiled and tested against Spark 2.3.4. Make sure you are using the most recent version of the connector.

In addition to adding the memsql-spark-connector, you will also need to have the MariaDB JDBC driver installed. This library is tested against the following MariaDB driver version:

"org.mariadb.jdbc" % "mariadb-java-client"  % "2.+"

Once you have everything installed, you're almost ready to run your first queries against MemSQL!

Configuration

The memsql-spark-connector is configurable globally via Spark options and locally when constructing a DataFrame. The options are named the same, however global options have the prefix spark.datasource.memsql..

Option Description
ddlEndpoint (required) Hostname or IP address of the MemSQL Master Aggregator in the format host[:port] (port is optional). Ex. master-agg.foo.internal:3308 or master-agg.foo.internal
dmlEndpoints Hostname or IP address of MemSQL Aggregator nodes to run queries against in the format host[:port],host[:port],... (port is optional, multiple hosts separated by comma). Ex. child-agg:3308,child-agg2 (default: ddlEndpoint)
user MemSQL username (default: root)
password MemSQL password (default: no password)
query The query to run (mutually exclusive with dbtable)
dbtable The table to query (mutually exclusive with query)
database If set, all connections will default to using this database (default: empty)
disablePushdown Disable SQL Pushdown when running queries (default: false)
enableParallelRead Enable reading data in parallel for some query shapes (default: false)
overwriteBehavior Specify the behavior during Overwrite; one of dropAndCreate, truncate, merge (default: dropAndCreate)
truncate ⚠️ Deprecated option, please use overwriteBehavior instead Truncate instead of drop an existing table during Overwrite (default: false)
loadDataCompression Compress data on load; one of (GZip, LZ4, Skip) (default: GZip)
loadDataFormat Serialize data on load; one of (Avro, CSV) (default: CSV)
tableKey Specify additional keys to add to tables created by the connector (See below for more details)
onDuplicateKeySQL If this option is specified, and a row is to be inserted that would result in a duplicate value in a PRIMARY KEY or UNIQUE index, MemSQL will instead perform an UPDATE of the old row. See examples below
insertBatchSize Size of the batch for row insertion (default: 10000)
maxErrors The maximum number of errors in a single LOAD DATA request. When this limit is reached, the load fails. If this property equals to 0, no error limit exists (Default: 0)

Examples

Example of configuring the memsql-spark-connector globally:

spark.conf.set("spark.datasource.memsql.ddlEndpoint", "memsql-master.cluster.internal")
spark.conf.set("spark.datasource.memsql.dmlEndpoints", "memsql-master.cluster.internal,memsql-child-1.cluster.internal:3307")
spark.conf.set("spark.datasource.memsql.user", "admin")
spark.conf.set("spark.datasource.memsql.password", "s3cur3-pa$$word")

Example of configuring the memsql-spark-connector using the read API:

val df = spark.read
    .format("memsql")
    .option("ddlEndpoint", "memsql-master.cluster.internal")
    .option("user", "admin")
    .load("foo")

Example of configuring the memsql-spark-connector using an external table in Spark SQL:

CREATE TABLE bar USING memsql OPTIONS ('ddlEndpoint'='memsql-master.cluster.internal','dbtable'='foo.bar')

For Java/Python versions of some of these examples, visit the section "Java & Python Example"

Writing to MemSQL

The memsql-spark-connector supports saving dataframe's to MemSQL using the Spark write API. Here is a basic example of using this API:

df.write
    .format("memsql")
    .option("loadDataCompression", "LZ4")
    .option("overwriteBehavior", "dropAndCreate")
    .mode(SaveMode.Overwrite)
    .save("foo.bar") // in format: database.table

If the target table ("foo" in the example above) does not exist in MemSQL the memsql-spark-connector will automatically attempt to create the table. If you specify SaveMode.Overwrite, if the target table already exists, it will be recreated or truncated before load. Specify overwriteBehavior = truncate to truncate rather than re-create.

Retrieving the number of written rows from taskMetrics

It is possible to add the listener and get the number of written rows.

spark.sparkContext.addSparkListener(new SparkListener() {
  override def onTaskEnd(taskEnd: SparkListenerTaskEnd) {
    println("Task id: " + taskEnd.taskInfo.id.toString)
    println("Records written: " + taskEnd.taskMetrics.outputMetrics.recordsWritten.toString)
  }
})

df.write.format("memsql").save("example")

Specifying keys for tables created by the Spark Connector

When creating a table, the memsql-spark-connector will read options prefixed with tableKey. These options must be formatted in a specific way in order to correctly specify the keys.

⚠️ The default table type is MemSQL Columnstore. If you want a RowStore table, you will need to specify a Primary Key using the tableKey option.

To explain we will refer to the following example:

df.write
    .format("memsql")
    .option("tableKey.primary", "id")
    .option("tableKey.key.created_firstname", "created, firstName")
    .option("tableKey.unique", "username")
    .mode(SaveMode.Overwrite)
    .save("foo.bar") // in format: database.table

In this example, we are creating three keys:

  1. A primary key on the id column
  2. A regular key on the columns created, firstname with the key name created_firstname
  3. A unique key on the username column

Note on (2): Any key can optionally specify a name, just put it after the key type. Key names must be unique.

To change the default ColumnStore sort key you can specify it explicitly:

df.write
    .option("tableKey.columnstore", "id")

You can also customize the shard key like so:

df.write
    .option("tableKey.shard", "id, timestamp")

Inserting rows into the table with ON DUPLICATE KEY UPDATE

When updating a rowstore table it is possible to insert rows with ON DUPLICATE KEY UPDATE option. See sql reference for more details.

df.write
    .option("onDuplicateKeySQL", "age = age + 1")
    .option("insertBatchSize", 300)
    .mode(SaveMode.Append)
    .save("foo.bar")

As a result of the following query, all new rows will be appended without changes. If the row with the same PRIMARY KEY or UNIQUE index already exists then the corresponding age value will be increased.

When you use ON DUPLICATE KEY UPDATE, all rows of the data frame are split into batches, and every insert query will contain no more than the specified insertBatchSize rows setting.

Save Modes

Save operations can optionally take a SaveMode, that specifies how to handle existing data if present. It is important to realize that these save modes do not utilize any locking and are not atomic. Additionally, when performing an Overwrite, the data will be deleted before writing out the new data.

  1. SaveMode.Append means that when saving a DataFrame to a data source, if data/table already exists, contents of the DataFrame are expected to be appended to existing data.
  2. SaveMode.Overwrite means that when saving a DataFrame to a data source, if data/table already exists, existing data is expected to be overwritten by the contents of the DataFrame.

Overwrite mode depends on overwriteBehavior option, for better understanding look at the section "Merging on save"

  1. SaveMode.ErrorIfExists means that when saving a DataFrame to a data source, if data already exists, an exception is expected to be thrown.
  2. SaveMode.Ignore means that when saving a DataFrame to a data source, if data already exists, the save operation is expected to not save the contents of the DataFrame and to not change the existing data.

Example of SaveMode option

df.write
    .mode(SaveMode.Append)
    .save("foo.bar")

Merging on save

When saving dataframes or datasets to MemSQL, you can manage how SaveMode.Overwrite is interpreted by the connector via the option overwriteBehavior. This option can take one of the following values:

  1. dropAndCreate(default) - drop and create the table before writing new values.
  2. truncate - truncate the table before writing new values.
  3. merge - replace rows with new rows by matching on the primary key. (Use this option only if you need to fully rewrite existing rows with new ones. If you need to specify some rule for update, use onDuplicateKeySQL option instead.)

All these options are case-insensitive.

Example of merge option

Suppose you have the following table, and the Id column is the primary key.

SELECT * FROM <table>;

Id Name Age
1 Alice 20
2 Bob 25
3 Charlie 30

If you save the following dataframe with overwriteBehavior = merge:

Id Name Age
2 Daniel 22
3 Eve 27
4 Franklin 35
df.write
    .format("memsql")
    .option("overwriteBehavior", "merge")
    .mode(SaveMode.Overwrite)
    .save("<yourdb>.<table>")

After the save is complete, the table will look like this:

note: rows with Id=2 and Id=3 were overwritten with new rows
note: the row with Id=1 was not touched and still exists in the result

SELECT * FROM <table>;

Id Name Age
1 Alice 20
2 Daniel 22
3 Eve 27
4 Franklin 35

SQL Pushdown

The memsql-spark-connector has extensive support for rewriting Spark SQL query plans into standalone MemSQL queries. This allows most of the computation to be pushed into the MemSQL distributed system without any manual intervention. The SQL rewrites are enabled automatically, but can be disabled either globally or per-query using the disablePushdown option.

⚠️ SQL Pushdown is either enabled or disabled on the entire Spark Session. If you want to run multiple queries in parallel with different values of disablePushdown, make sure to run them on separate Spark Sessions.

We currently support most of the primary Logical Plan nodes in Spark SQL including:

  • Project
  • Filter
  • Aggregate
  • Window
  • Join
  • Limit
  • Sort

We also support most Spark SQL expressions. A full list of supported operators/functions can be found in the file ExpressionGen.scala.

The best place to look for examples of fully supported queries is in the tests. Check out this file as a starting point: SQLPushdownTest.scala.

Debugging SQL Pushdown

If you encounter an issue with SQL Pushdown the first step is to look at the explain. You can do this easily from any dataframe using the function df.explain(). If you pass the argument true you will get a lot more output that includes pre and post optimization passes.

In addition, the memsql-spark-connector outputs a lot of helpful information when the TRACE log level is enabled for the com.memsql.spark package. You can do this in your log4j configuration by adding the following line:

log4j.logger.com.memsql.spark=TRACE

Make sure not to leave it in place since it generates a huge amount of tracing output.

Parallel Read Support

If you enable parallel reads via the enableParallelRead option, the memsql-spark-connector will attempt to read results directly from MemSQL leaf nodes. This can drastically improve performance in some cases.

⚠️ Parallel reads are not consistent

Parallel reads read directly from partitions on the leaf nodes which skips our entire transaction layer. This means that the individual reads will see an independent version of the databases distributed state. Make sure to take this into account when enabling parallel read.

⚠️ Parallel reads transparently fallback to single stream reads

Parallel reads currently only work for query-shapes which do no work on the Aggregator and thus can be pushed entirely down to the leaf nodes. To determine if a particular query is being pushed down you can ask the dataframe how many partitions it has like so:

df.rdd.getNumPartitions

If this value is > 1 then we are reading in parallel from leaf nodes.

⚠️ Parallel reads require consistent authentication and connectible leaf nodes

In order to use parallel reads, the username and password provided to the memsql-spark-connector must be the same across all nodes in the cluster.

In addition, the hostnames and ports listed by SHOW LEAVES must be directly connectible from Spark.

Security

Connecting with a Kerberos-authenticated User

You can use the MemSQL Spark Connector with a Kerberized user without any additional configuration. To use a Kerberized user, you need to configure the connector with the given MemSQL database user that is authenticated with Kerberos (via the user option). Please visit our documentation here to learn about how to configure MemSQL users with Kerberos.

Here is an example of configuring the Spark connector globally with a Kerberized MemSQL user called krb_user.

spark = SparkSession.builder()
    .config(“spark.datasource.memsql.user”, “krb_user”)
    .getOrCreate()

You do not need to provide a password when configuring a Spark Connector user that is Kerberized. The connector driver (MariaDB) will be able to authenticate the Kerberos user from the cache by the provided username. Other than omitting a password with this configuration, using a Kerberized user with the Connector is no different than using a standard user. Note that if you do provide a password, it will be ignored.

SQL Permissions

MemSQL has a permission matrix which describes the permissions required to run each command.

To make any SQL operations through Spark connector you should have different permissions for different type of operation. The matrix below describes the minimum permissions you should have to perform some operation. As alternative to minimum required permissions, ALL PRIVILEGES allow you to perform any operation.

Operation Min. Permission Alternative Permission
READ from collection SELECT ALL PRIVILEGES
WRITE to collection SELECT, INSERT ALL PRIVILEGES
DROP database or collection SELECT, INSERT, DROP ALL PRIVILEGES
CREATE database or collection SELECT, INSERT, CREATE ALL PRIVILEGES

For more information on GRANTING privileges, see this documentation

SSL Support

The MemSQL Spark Connector uses the MariaDB JDBC Driver under the hood and thus supports SSL configuration out of the box. In order to configure SSL, first ensure that your MemSQL cluster has SSL configured. Documentation on how to set this up can be found here: https://docs.memsql.com/latest/guides/security/encryption/ssl/

Once you have setup SSL on your server, you can enable SSL via setting the following options:

spark.conf.set("spark.datasource.memsql.useSSL", "true")
spark.conf.set("spark.datasource.memsql.serverSslCert", "PATH/TO/CERT")

Note: the serverSslCert option may be server's certificate in DER form, or the server's CA certificate. Can be used in one of 3 forms:

  • serverSslCert=/path/to/cert.pem (full path to certificate)
  • serverSslCert=classpath:relative/cert.pem (relative to current classpath)
  • or as verbatim DER-encoded certificate string ------BEGIN CERTIFICATE-----...

You may also want to set these additional options depending on your SSL configuration:

spark.conf.set("spark.datasource.memsql.trustServerCertificate", "true")
spark.conf.set("spark.datasource.memsql.disableSslHostnameVerification", "true")

More information on the above parameters can be found at MariaDB's documentation for their JDBC driver here: https://mariadb.com/kb/en/about-mariadb-connector-j/#tls-parameters

Filing issues

When filing issues please include as much information as possible as well as any reproduction steps. It's hard for us to reproduce issues if the problem depends on specific data in your MemSQL table for example. Whenever possible please try to construct a minimal reproduction of the problem and include the table definition and table contents in the issue.

If the issue is related to SQL Pushdown (or you aren't sure) make sure to include the TRACE output (from the com.memsql.spark package) or the full explain of the plan. See the debugging SQL Pushdown section above for more information on how to do this.

Happy querying!

Setting up development environment

  • install Oracle JDK 8 from this url: https://www.oracle.com/java/technologies/javase/javase-jdk8-downloads.html
  • install community version of Intellij IDEA from https://www.jetbrains.com/idea/
  • clone the repository https://github.com/memsql/memsql-spark-connector.git
  • in Intellij IDEA choose Configure->Plugins and install Scala plugin
  • in Intellij IDEA run Import Project and select path to memsql-spark-connector
  • choose import project from external model and sbt
  • in Project JDK select New...->JDK and choose path to the installed JDK
  • Finish
  • it will overwrite some files and create build files (which are in gitignore)
  • in Intellij IDEA choose File->Close Project
  • run git checkout . to revert all changes made by Intellij IDEA
  • in Intellij IDEA choose Open and select path to memsql-spark-connector
  • run Test Spark 2.3 (it should succeed)

SQL Pushdown Incompatibilities

  • ToUnixTimestamp and UnixTimestamp handle only time less then 2038-01-19 03:14:08, if they get DateType or TimestampType as a first argument
  • FromUnixTime with default format (yyyy-MM-dd HH:mm:ss) handle only time less then 2147483648 (2^31)

Major changes from the 2.0.0 connector

The MemSQL Spark Connector 3.0.4 has a number of key features and enhancements:

  • Introduces SQL Optimization & Rewrite for most query shapes and compatible expressions
  • Implemented as a native Spark SQL plugin
  • Supports both the DataSource and DataSourceV2 API for maximum support of current and future functionality
  • Contains deep integrations with the Catalyst query optimizer
  • Is compatible with Spark 2.3 and 2.4
  • Leverages MemSQL LOAD DATA to accelerate ingest from Spark via compression, vectorized cpu instructions, and optimized segment sizes
  • Takes advantage of all the latest and greatest features in MemSQL 7.x

Java & Python Examples

Java

Configuration

SparkConf conf = new SparkConf();
conf.set("spark.datasource.memsql.ddlEndpoint", "memsql-master.cluster.internal")
conf.set("spark.datasource.memsql.dmlEndpoints", "memsql-master.cluster.internal,memsql-child-1.cluster.internal:3307")
conf.set("spark.datasource.memsql.user", "admin")
conf.set("spark.datasource.memsql.password", "s3cur3-pa$$word")

Read Data

DataFrame df = spark
  .read()
  .format("memsql")
  .option("ddlEndpoint", "memsql-master.cluster.internal")
  .option("user", "admin")
  .load("foo");

Write Data

df.write()
    .format("memsql")
    .option("loadDataCompression", "LZ4")
    .option("overwriteBehavior", "dropAndCreate")
    .mode(SaveMode.Overwrite)
    .save("foo.bar")

Python

Configuration

spark.conf.set("spark.datasource.memsql.ddlEndpoint", "memsql-master.cluster.internal")
spark.conf.set("spark.datasource.memsql.dmlEndpoints", "memsql-master.cluster.internal,memsql-child-1.cluster.internal:3307")
spark.conf.set("spark.datasource.memsql.user", "admin")
spark.conf.set("spark.datasource.memsql.password", "s3cur3-pa$$word")

Read Data

df = spark \
  .read \
  .format("memsql") \
  .option("ddlEndpoint", "memsql-master.cluster.internal") \
  .option("user", "admin") \
  .load("foo")

Write Data

df.write \
    .format("memsql") \
    .option("loadDataCompression", "LZ4") \
    .option("overwriteBehavior", "dropAndCreate") \
    .mode("overwrite") \
    .save("foo.bar")