JarCastingMemSQL Spark Connector
Version: 3.0.5 
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_2.11:3.0.5-spark-2.4.4
We release two versions of the memsql-spark-connector, one per Spark version. An example version number is: 3.0.5-spark-2.3.4 which is the 3.0.5 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 |
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 dataframes 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:
- A primary key on the
idcolumn - A regular key on the columns
created, firstnamewith the key namecreated_firstname - A unique key on the
usernamecolumn
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.
SaveMode.Appendmeans 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.SaveMode.Overwritemeans 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.
Overwritemode depends onoverwriteBehavioroption, for better understanding look at the section "Merging on save"
SaveMode.ErrorIfExistsmeans that when saving a DataFrame to a data source, if data already exists, an exception is expected to be thrown.SaveMode.Ignoremeans 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:
dropAndCreate(default) - drop and create the table before writing new values.truncate- truncate the table before writing new values.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, useonDuplicateKeySQLoption 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 ofdisablePushdown, 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 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 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.
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.
Running SQL queries
The methods executeMemsqlQuery(query: String, variables: Any*) and executeMemsqlQueryDB(db: String, query: String, variables: Any*) allow you to run SQL queries on a MemSQL database directly using the existing SparkSession object. The method executeMemsqlQuery uses the database defined in the SparkContext object you use. executeMemsqlQueryDB allows you to specify the database that will be used for querying. The following examples demonstrate their usage (assuming you already have initialized SparkSession object named spark). The methods return Iterator[org.apache.spark.sql.Row] object.
// this imports the implicit class QueryMethods which adds the methods
// executeMemsqlQuery and executeMemsqlQueryDB to SparkSession class
import com.memsql.spark.SQLHelper.QueryMethods
// You can pass an empty database to executeMemsqlQueryDB to connect to MemSQL without specifying a database.
// This allows you to create a database which is defined in the SparkSession config for example.
spark.executeMemsqlQueryDB("", "CREATE DATABASE foo")
// the next query can be used if the database field has been specified in spark object
s = spark.executeMemsqlQuery("CREATE TABLE user(id INT, name VARCHAR(30), status BOOLEAN)")
// you can create another database
spark.executeMemsqlQuery("CREATE DATABASE bar")
// the database specified as the first argument will override the database set in the SparkSession object
s = spark.executeMemsqlQueryDB("bar", "CREATE TABLE user(id INT, name VARCHAR(30), status BOOLEAN)")
You can pass query parameters to the functions as arguments following query. The supported types for parameters are String, Int, Long, Short, Float, Double, Boolean, Byte, java.sql.Date, java.sql.Timestamp.
import com.memsql.spark.SQLHelper.QueryMethods
var userRows = spark.executeMemsqlQuery("SELECT id, name FROM USER WHERE id > ? AND status = ? AND name LIKE ?", 10, true, "%at%")
for (row <- userRows) {
println(row.getInt(0), row.getString(1))
}
Alternatively, these functions can take SparkSession object as the first argument, as in the example below
import com.memsql.spark.SQLHelper.{executeMemsqlQuery, executeMemsqlQueryDB}
executeMemsqlQuery(spark, "CREATE DATABASE foo")
var s = executeMemsqlQueryDB(spark, "foo", "SHOW TABLES")
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 the community edition 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->Pluginsand install Scala plugin - in Intellij IDEA run
Import Projectand select path to memsql-spark-connectorbuild.sbtfile - choose
import project from external modelandsbt - in
Project JDKselectNew...->JDKand choose the path to the installed JDK Finish- it will overwrite some files and create build files (which are in .gitignore)
- you may need to remove the
.ideadirectory for IDEA to load the project properly - in Intellij IDEA choose
File->Close Project - run
git checkout .to revert all changes made by Intellij IDEA - in Intellij IDEA choose
Openand select path to memsql-spark-connector - run
Test Spark 2.3(it should succeed)
SQL Pushdown Incompatibilities
ToUnixTimestampandUnixTimestamphandle only time less then2038-01-19 03:14:08, if they getDateTypeorTimestampTypeas a first argumentFromUnixTimewith default format (yyyy-MM-dd HH:mm:ss) handle only time less then2147483648(2^31)DecimalTypeon the overflow is truncated (by default spark either throws exception or returns null)greatestandleastreturn null if at least one argument is null (in spark these functions skip nulls)- When string is casted to numeric type, memsql takes the prefix of it which is numeric (spark returns
nullif the whole string is not numeric) - When numeric type is casted to the smaller one memsql truncates it. For example
500casted to the Byte will be127Note: spark optimizer can optimize casts for literals and then behaviour for them will match custom spark behaviour - When fractional type is casted to integral type memsql rounds it to the closest value
Loginstead ofNaN,Infinity,-InfinityreturnsnullRoundrounds down, if the number that should be rounded is followed by 5 and it isDOUBLEorFLOAT(DECIMALwill be rounded up)Convworks differently if the number contains non alphanumeric charactersShiftLeft,ShiftRightandShiftRightUnsignedconverts the value to the UNSIGNED BIGINT and then produces the shift In the case of overflow, it returns 0 (1<<64=0and10>>20=0)
Major changes from the 2.0.0 connector
The MemSQL Spark Connector 3.0.5 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")
