Spark学习笔记——读写ScyllaDB

Scylla兼容cassandra API，所以可以使用spark读写cassandra的方法来进行读写

1.查看scyllaDB对应的cassandra版本

cqlsh:my_db> SHOW VERSION
[cqlsh 5.0.1 | Cassandra 3.0.8 | CQL spec 3.3.1 | Native protocol v4]

2.查看spark和cassandra对应的版本

参考：https://github.com/datastax/spark-cassandra-connector

3.写scyllaDB

dataset API写scyllaDB

ds2.write
      .mode("append")
      .format("org.apache.spark.sql.cassandra")
      .options(Map("table" -> "my_tb", "keyspace" -> "my_db", "output.consistency.level" -> "ALL", "ttl" -> "8640000"))
      .save()

RDD API写scyllaDB

import com.datastax.oss.driver.api.core.ConsistencyLevel
import com.datastax.spark.connector._

ds.rdd.saveToCassandra("my_db", "my_tb", writeConf = WriteConf(ttl = TTLOption.constant(8640000), consistencyLevel = ConsistencyLevel.ALL))

注意字段的数量和顺序需要和ScyllaDB表的顺序一致，可以使用下面方式select字段

val columns = Seq[String](
      "a",
      "b",
      "c")
val colNames = columns.map(name => col(name))
val colRefs = columns.map(name => toNamedColumnRef(name))

val df2 = df.select(colNames: _*)
df2.rdd
      .saveToCassandra(ks, table, SomeColumns(colRefs: _*), writeConf = WriteConf(ttl = TTLOption.constant(8640000), consistencyLevel = ConsistencyLevel.ALL))

不过官方推荐使用DataFrame API，而不是RDD API

If you have the option we recommend using DataFrames instead of RDDs

https://github.com/datastax/spark-cassandra-connector/blob/master/doc/4_mapper.md

4.读scyllaDB

val df = spark
  .read
  .format("org.apache.spark.sql.cassandra")
  .options(Map( "table" -> "words", "keyspace" -> "test" ))
  .load()

　　

参考：通过 Spark 创建/插入数据到 Azure Cosmos DB Cassandra API

Cassandra Optimizations for Apache Spark

5.cassandra connector参数

https://github.com/datastax/spark-cassandra-connector/blob/master/doc/reference.md

参数调优：Spark + Cassandra, All You Need to Know: Tips and Optimizations