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Documentation

Datasets (Previously DataFrames)

Datasets provide a new API for manipulating data within Spark. These provide a more user friendly experience than pure Scala for common queries. The Spark Cassandra Connector provides an integrated Data Source to make creating Cassandra Datasets easy.

What happened to DataFrames?

Spark Docs:

Datasource Specific Options

DataSources in Spark take a map of Options which define how the source should act. The Connector provides a CassandraSource which recognizes the following key/value pairs. Those followed with a default of N/A are required, all others are optional.

Option Key Controls Values Default
table The Cassandra table to connect to String N/A
keyspace The keyspace where table is looked for String N/A
cluster The group of the Cluster Level Settings to inherit String "default"
pushdown Enables pushing down predicates to Cassandra when applicable (true,false) true

General Read, Write and Connection Options

Any normal Spark Connector configuration options for Connecting, Reading or Writing can be passed through as Dataset options as well. When using the read command below these options should appear exactly the same as when set in the SparkConf. See Config Helpers for typed helpers for setting these options.

Setting Cluster and Keyspace Level Options

The connector also provides a way to describe the options which should be applied to all Datasets within a cluster or within a keyspace. When a property has been specified at the table level it will override the default keyspace or cluster property.

To add these properties add keys to your SparkConf use the helpers explained in the next section or by manually entering them in the format

clusterName:keyspaceName/propertyName

Example Using TypeSafe Parameter Configuration Options

There are also some helper methods which simplify setting Spark Cassandra Connector related parameters. This makes it easier to set parameters without remembering the above syntax:

import org.apache.spark.sql.cassandra._

import com.datastax.spark.connector.cql.CassandraConnectorConf
import com.datastax.spark.connector.rdd.ReadConf

// set params for all clusters and keyspaces
spark.setCassandraConf(CassandraConnectorConf.KeepAliveMillisParam.option(10000))

// set params for the particular cluster
spark.setCassandraConf("Cluster1", CassandraConnectorConf.ConnectionHostParam.option("127.0.0.1") ++ CassandraConnectorConf.ConnectionPortParam.option(12345))
spark.setCassandraConf("Cluster2", CassandraConnectorConf.ConnectionHostParam.option("127.0.0.2"))

// set params for the particular keyspace 
spark.setCassandraConf("Cluster1", "ks1", ReadConf.SplitSizeInMBParam.option(128))
spark.setCassandraConf("Cluster1", "ks2", ReadConf.SplitSizeInMBParam.option(64))
spark.setCassandraConf("Cluster2", "ks3", ReadConf.SplitSizeInMBParam.option(80))

Example Changing Cluster/Keyspace Level Properties

spark.setCassandraConf("ClusterOne", "ks1", ReadConf.SplitSizeInMBParam.option(32))
spark.setCassandraConf("default", "test", ReadConf.SplitSizeInMBParam.option(128))

val df = spark
  .read
  .format("org.apache.spark.sql.cassandra")
  .options(Map( "table" -> "words", "keyspace" -> "test"))
  .load() // This Dataset will use a spark.cassandra.input.size of 128

val otherdf =  spark
  .read
  .format("org.apache.spark.sql.cassandra")
  .options(Map( "table" -> "words", "keyspace" -> "test" , "cluster" -> "ClusterOne"))
  .load() // This Dataset will use a spark.cassandra.input.size of 32

val lastdf = spark
  .read
  .format("org.apache.spark.sql.cassandra")
  .options(Map(
    "table" -> "words",
    "keyspace" -> "test" ,
    "cluster" -> "ClusterOne",
    "spark.cassandra.input.split.size_in_mb" -> 48
    )
  ).load() // This Dataset will use a spark.cassandra.input.split.size of 48

Creating Datasets using Read Commands

The most programmatic way to create a Dataset is to invoke a read command on the SparkSession. This will build a DataFrameReader. Specify format as org.apache.spark.sql.cassandra. You can then use options to give a map of Map[String,String] of options as described above. Then finish by calling load to actually get a Dataset. This code is all lazy and will not actually load any data until an action is called.

As well as specifying all these parameters manually, we offer a set of helper functions to make this easier as well.

Example Creating a Dataset using a Read Command

val df = spark
  .read
  .format("org.apache.spark.sql.cassandra")
  .options(Map( "table" -> "words", "keyspace" -> "test" ))
  .load()
  
df.show
word count
cat  30
fox  40

There are also some helper methods which can make creating Datasets easier. They can be accessed after importing org.apache.spark.sql.cassandra package. In the following example, all the commands used to create the Dataset are equivalent:

Example Using Format Helper Functions

import org.apache.spark.sql.cassandra._

val df = spark
  .read
  .cassandraFormat("words", "test")
  .load()
 
//Loading an Dataset using a format helper and a option helper
val df = spark
  .read
  .cassandraFormat("words", "test")
  .options(ReadConf.SplitSizeInMBParam.option(32))
  .load()

Creating Datasets using Spark SQL

Accessing Datasets using Spark SQL involves creating temporary views with the format as org.apache.spark.sql.cassandra. The OPTIONS passed to this table are used to establish a relation between the CassandraTable and the Spark catalog reference.

Example Creating a Source Using Spark SQL:

Create Relation with the Cassandra table test.words

val createDDL = """CREATE TEMPORARY VIEW words
     USING org.apache.spark.sql.cassandra
     OPTIONS (
     table "words",
     keyspace "test",
     cluster "Test Cluster",
     pushdown "true")"""
spark.sql(createDDL) // Creates Catalog Entry registering an existing Cassandra Table
spark.sql("SELECT * FROM words").show
spark.sql("SELECT * FROM words WHERE word = 'fox'").show

Persisting a Dataset to Cassandra Using the Save Command

Datasets provide a save function which allows them to persist their data to another DataSource. The connector supports using this feature to persist a Dataset to a Cassandra table.

Example Copying Between Two Tables Using Datasets

val df = spark
  .read
  .cassandraFormat("words", "test")
  .load()

df.write
  .cassandraFormat("words_copy", "test")
  .save()

Similarly to reading Cassandra tables into Datasets, we have some helper methods for the write path which are provided by org.apache.spark.sql.cassandra package. In the following example, all the commands are equivalent:

Example Using Helper Commands to Write Datasets

import org.apache.spark.sql.cassandra._

df.write
  .format("org.apache.spark.sql.cassandra")
  .options(Map("table" -> "words_copy", "keyspace" -> "test", "cluster" -> "cluster_B"))
  .save()

df.write
  .cassandraFormat("words_copy", "test", "cluster_B")
  .save()

Setting Connector Specific Options on Datasets

Connector specific options can be set by invoking options method on either DataFrameReader or DataFrameWriter. There are several settings you may want to change in ReadConf, WriteConf, CassandraConnectorConf, AuthConf and others. Those settings are identified by instances of ConfigParameter case class which offers an easy way to apply the option which it represents to a DataFrameReader or DataFrameWriter.

Suppose we want to set spark.cassandra.read.timeout_ms to 7 seconds on some DataFrameReader, we can do this both ways:

option("spark.cassandra.read.timeout_ms", "7000")

Since this setting is represented by CassandraConnectorConf.ReadTimeoutParam we can simply do:

options(CassandraConnectorConf.ReadTimeoutParam.sqlOption("7000"))

Each parameter, that is, each instance of ConfigParameter allows to invoke apply method with a single parameter. That method returns a Map[String, String] (note that you need to use options instead of option) so setting multiple parameters can be chained:

options(CassandraConnectorConf.ReadTimeoutParam.sqlOption("7000") ++ ReadConf.TaskMetricParam.sqlOption(true))

Creating a New Cassandra Table From a Dataset Schema

Spark Cassandra Connector adds a method to Dataset that allows it to create a new Cassandra table from the StructType schema of the Dataset. This is convenient for persisting a Dataset to a new table, especially when the schema of the Dataset is not known (fully or at all) ahead of time (at compile time of your application). Once the new table is created, you can persist the Dataset to the new table using the save function described above.

The partition key and clustering key of the newly generated table can be set by passing in a list of names of columns which should be used as partition key and clustering key.

Example Creating a Cassandra Table from a Dataset

// Add spark connector specific methods to Dataset
import com.datastax.spark.connector._

val df = spark
  .read
  .cassandraFormat("words", "test")
  .load()

val renamed = df.withColumnRenamed("col1", "newcolumnname")
renamed.createCassandraTable(
    "test", 
    "renamed", 
    partitionKeyColumns = Some(Seq("user")), 
    clusteringKeyColumns = Some(Seq("newcolumnname")))

renamed.write
  .cassandraFormat("renamed", "test")
  .save()

Automatic Predicate Pushdown and Column Pruning

The Dataset API will automatically pushdown valid "where" clauses to Cassandra as long as the pushdown option is enabled (default is enabled).

Example Table

CREATE KEYSPACE test WITH replication = {'class': 'SimpleStrategy', 'replication_factor': 1 };
USE test;
CREATE table words (
    user  TEXT, 
    word  TEXT, 
    count INT, 
    PRIMARY KEY (user, word));

INSERT INTO words (user, word, count ) VALUES ( 'Russ', 'dino', 10 );
INSERT INTO words (user, word, count ) VALUES ( 'Russ', 'fad', 5 );
INSERT INTO words (user, word, count ) VALUES ( 'Sam', 'alpha', 3 );
INSERT INTO words (user, word, count ) VALUES ( 'Zebra', 'zed', 100 );

First we can create a Dataset and see that it has no pushdown filters set in the log. This means all requests will go directly to Cassandra and we will require reading all of the data to show this Dataset.

Example Catalyst Optimization with Cassandra Server Side Pushdowns

val df = spark
  .read
  .cassandraFormat("words", "test")
  .load
df.explain
15/07/06 09:21:21 INFO CassandraSourceRelation: filters:
15/07/06 09:21:21 INFO CassandraSourceRelation: pushdown filters: //ArrayBuffer()
== Physical Plan ==
PhysicalRDD [user#0,word#1,count#2], MapPartitionsRDD[2] at explain //at <console>:22

df.show
15/07/06 09:26:03 INFO CassandraSourceRelation: filters:
15/07/06 09:26:03 INFO CassandraSourceRelation: pushdown filters: //ArrayBuffer()

+-----+-----+-----+
| user| word|count|
+-----+-----+-----+
|Zebra|  zed|  100|
| Russ| dino|   10|
| Russ|  fad|    5|
|  Sam|alpha|    3|
+-----+-----+-----+

The example schema has a clustering key of "word" so we can pushdown filters on that column to Cassandra. We do this by applying a normal Dataset filter. The connector will automatically determine that the filter can be pushed down and will add it to pushdown filters. All of the elements of pushdown filters will be automatically added to the CQL requests made to Cassandra for the data from this table. The subsequent call will then only serialize data from Cassandra which passes the filter, reducing the load on Cassandra.

val dfWithPushdown = df.filter(df("word") > "ham")
dfWithPushdown.explain
15/07/06 09:29:10 INFO CassandraSourceRelation: filters: GreaterThan(word,ham)
15/07/06 09:29:10 INFO CassandraSourceRelation: pushdown filters: ArrayBuffer(GreaterThan(word,ham))
== Physical Plan ==
Filter (word#1 > ham)
 PhysicalRDD [user#0,word#1,count#2], MapPartitionsRDD[18] at explain at <console>:24

dfWithPushdown.show
15/07/06 09:30:48 INFO CassandraSourceRelation: filters: GreaterThan(word,ham)
15/07/06 09:30:48 INFO CassandraSourceRelation: pushdown filters: ArrayBuffer(GreaterThan(word,ham))
+-----+----+-----+
| user|word|count|
+-----+----+-----+
|Zebra| zed|  100|
+-----+----+-----+

Example Pushdown Filters

Example table

CREATE KEYSPACE IF NOT EXISTS pushdowns WITH replication = { 'class' : 'SimpleStrategy', 'replication_factor' : 3 };
USE pushdowns;

CREATE TABLE pushdownexample (
    partitionkey1 BIGINT,
    partitionkey2 BIGINT,
    partitionkey3 BIGINT,
    clusterkey1   BIGINT,
    clusterkey2   BIGINT,
    clusterkey3   BIGINT,
    regularcolumn BIGINT,
    PRIMARY KEY ((partitionkey1, partitionkey2, partitionkey3), clusterkey1, clusterkey2, clusterkey3)
);
val df = spark
  .read
  .cassandraFormat("pushdownexample", "pushdowns")
  .load()

To push down partition keys, all of them must be included, but not more than one predicate per partition key, otherwise nothing is pushed down.

df.filter("partitionkey1 = 1 AND partitionkey2 = 1 AND partitionkey3 = 1").show()
INFO  2015-08-26 00:37:40 org.apache.spark.sql.cassandra.CassandraSourceRelation: filters: EqualTo(partitionkey1,1), EqualTo(partitionkey2,1), EqualTo(partitionkey3,1)
INFO  2015-08-26 00:37:40 org.apache.spark.sql.cassandra.CassandraSourceRelation: pushdown filters: ArrayBuffer(EqualTo(partitionkey1,1), EqualTo(partitionkey2,1), EqualTo(partitionkey3,1))

One partition key left out:

df.filter("partitionkey1 = 1 AND partitionkey2 = 1").show()
INFO  2015-08-26 00:53:07 org.apache.spark.sql.cassandra.CassandraSourceRelation: filters: EqualTo(partitionkey1,1), EqualTo(partitionkey2,1)
INFO  2015-08-26 00:53:07 org.apache.spark.sql.cassandra.CassandraSourceRelation: pushdown filters: ArrayBuffer()

More than one predicate for partitionkey3:

df.filter("partitionkey1 = 1 AND partitionkey2 = 1 AND partitionkey3 > 0 AND partitionkey3 < 5").show()
INFO  2015-08-26 00:54:03 org.apache.spark.sql.cassandra.CassandraSourceRelation: filters: EqualTo(partitionkey1,1), EqualTo(partitionkey2,1), GreaterThan(partitionkey3,0), LessThan(partitionkey3,5)
INFO  2015-08-26 00:54:03 org.apache.spark.sql.cassandra.CassandraSourceRelation: pushdown filters: ArrayBuffer()

Clustering keys are more relaxed. But only the last predicate can be non-EQ, and if there is more than one predicate for a column, they must not be EQ or IN, otherwise only some predicates may be pushed down.

df.filter("clusterkey1 = 1 AND clusterkey2 > 0 AND clusterkey2 < 10").show()
INFO  2015-08-26 01:01:02 org.apache.spark.sql.cassandra.CassandraSourceRelation: filters: EqualTo(clusterkey1,1), GreaterThan(clusterkey2,0), LessThan(clusterkey2,10)
INFO  2015-08-26 01:01:02 org.apache.spark.sql.cassandra.CassandraSourceRelation: pushdown filters: ArrayBuffer(EqualTo(clusterkey1,1), GreaterThan(clusterkey2,0), LessThan(clusterkey2,10))

First predicate not EQ:

df.filter("clusterkey1 > 1 AND clusterkey2 > 1").show()
INFO  2015-08-26 00:55:01 org.apache.spark.sql.cassandra.CassandraSourceRelation: filters: GreaterThan(clusterkey1,1), GreaterThan(clusterkey2,1)
INFO  2015-08-26 00:55:01 org.apache.spark.sql.cassandra.CassandraSourceRelation: pushdown filters: ArrayBuffer(GreaterThan(clusterkey1,1))

clusterkey2 EQ predicate:

df.filter("clusterkey1 = 1 AND clusterkey2 = 1 AND clusterkey2 < 10").show()
INFO  2015-08-26 00:56:37 org.apache.spark.sql.cassandra.CassandraSourceRelation: filters: EqualTo(clusterkey1,1), EqualTo(clusterkey2,1), LessThan(clusterkey2,10)
INFO  2015-08-26 00:56:37 org.apache.spark.sql.cassandra.CassandraSourceRelation: pushdown filters: ArrayBuffer(EqualTo(clusterkey1,1), EqualTo(clusterkey2,1))

What Happened to DataFrames?

In Spark 2.0 DataFrames are now just a specific case of the Dataset API. In particular a DataFrame is just an alias for Dataset[Row]. This means everything you know about DataFrames is also applicable to Datasets. A DataFrame is just a special Dataset that is made up of Row objects. Many texts and resources still use the two terms interchangeably.

Next - Python DataFrames