通过

通过 Spark 对 Azure Cosmos DB for Apache Cassandra 执行的表复制操作

  • 项目

适用对象： Cassandra

本文介绍如何通过 Spark 在 Azure Cosmos DB for Apache Cassandra 中的表之间复制数据。 本文中描述的命令还可用于将数据从 Apache Cassandra 表复制到 Azure Cosmos DB for Apache Cassandra 表。

API for Cassandra 配置

在笔记本群集中设置以下 spark 配置。 这是一次性活动。

//Connection-related
 spark.cassandra.connection.host  YOUR_ACCOUNT_NAME.cassandra.cosmosdb.azure.cn  
 spark.cassandra.connection.port  10350  
 spark.cassandra.connection.ssl.enabled  true  
 spark.cassandra.auth.username  YOUR_ACCOUNT_NAME  
 spark.cassandra.auth.password  YOUR_ACCOUNT_KEY  
// if using Spark 2.x
// spark.cassandra.connection.factory  com.microsoft.azure.cosmosdb.cassandra.CosmosDbConnectionFactory  

//Throughput-related...adjust as needed
 spark.cassandra.output.batch.size.rows  1  
// spark.cassandra.connection.connections_per_executor_max  10   // Spark 2.x
 spark.cassandra.connection.remoteConnectionsPerExecutor  10   // Spark 3.x
 spark.cassandra.output.concurrent.writes  1000  
 spark.cassandra.concurrent.reads  512  
 spark.cassandra.output.batch.grouping.buffer.size  1000  
 spark.cassandra.connection.keep_alive_ms  600000000

注意

如果使用的是 Spark 3.x，则无需安装 Azure Cosmos DB 帮助程序和连接工厂。 对于 Spark 3 连接器，还应该使用 remoteConnectionsPerExecutor 而不是 connections_per_executor_max（见上文）。

警告

本文展示的 Spark 3 示例已使用 Spark 3.2.1 版本和相应的 Cassandra Spark 连接器 com.datastax.spark:spark-cassandra-connector-assembly_2.12:3.2.0 测试过。 更高版本的 Spark 和/或 Cassandra 连接器可能无法正常运行。

插入示例数据

import org.apache.spark.sql.cassandra._
//Spark connector
import com.datastax.spark.connector._
import com.datastax.spark.connector.cql.CassandraConnector

//if using Spark 2.x, CosmosDB library for multiple retry
//import com.microsoft.azure.cosmosdb.cassandra

val booksDF = Seq(
   ("b00001", "Arthur Conan Doyle", "A study in scarlet", 1887,11.33),
   ("b00023", "Arthur Conan Doyle", "A sign of four", 1890,22.45),
   ("b01001", "Arthur Conan Doyle", "The adventures of Sherlock Holmes", 1892,19.83),
   ("b00501", "Arthur Conan Doyle", "The memoirs of Sherlock Holmes", 1893,14.22),
   ("b00300", "Arthur Conan Doyle", "The hounds of Baskerville", 1901,12.25)
).toDF("book_id", "book_author", "book_name", "book_pub_year","book_price")

booksDF.write
  .mode("append")
  .format("org.apache.spark.sql.cassandra")
  .options(Map( "table" -> "books", "keyspace" -> "books_ks", "output.consistency.level" -> "ALL", "ttl" -> "10000000"))
  .save()

在表之间复制数据

在表之间复制数据（存在目标表）

//1) Create destination table
val cdbConnector = CassandraConnector(sc)
cdbConnector.withSessionDo(session => session.execute("CREATE TABLE IF NOT EXISTS books_ks.books_copy(book_id TEXT PRIMARY KEY,book_author TEXT, book_name TEXT,book_pub_year INT,book_price FLOAT) WITH cosmosdb_provisioned_throughput=4000;"))

//2) Read from one table
val readBooksDF = sqlContext
  .read
  .format("org.apache.spark.sql.cassandra")
  .options(Map( "table" -> "books", "keyspace" -> "books_ks"))
  .load

//3) Save to destination table
readBooksDF.write
  .cassandraFormat("books_copy", "books_ks", "")
  .save()

//4) Validate copy to destination table
sqlContext
  .read
  .format("org.apache.spark.sql.cassandra")
  .options(Map( "table" -> "books_copy", "keyspace" -> "books_ks"))
  .load
  .show

在表之间复制数据（不存在目标表）

import com.datastax.spark.connector._

//1) Read from source table
val readBooksDF = sqlContext
  .read
  .format("org.apache.spark.sql.cassandra")
  .options(Map( "table" -> "books", "keyspace" -> "books_ks"))
  .load

//2) Creates an empty table in the keyspace based off of source table
val newBooksDF = readBooksDF
newBooksDF.createCassandraTable(
    "books_ks", 
    "books_new", 
    partitionKeyColumns = Some(Seq("book_id"))
    //clusteringKeyColumns = Some(Seq("some column"))
    )

//3) Saves the data from the source table into the newly created table
newBooksDF.write
  .cassandraFormat("books_new", "books_ks","")
  .mode(SaveMode.Append)
  .save()

//4) Validate table creation and data load
sqlContext
  .read
  .format("org.apache.spark.sql.cassandra")
  .options(Map( "table" -> "books_new", "keyspace" -> "books_ks"))
  .load
  .show

输出-

+-------+------------------+--------------------+----------+-------------+
|book_id|       book_author|           book_name|book_price|book_pub_year|
+-------+------------------+--------------------+----------+-------------+
| b00300|Arthur Conan Doyle|The hounds of Bas...|     12.25|         1901|
| b00001|Arthur Conan Doyle|  A study in scarlet|     11.33|         1887|
| b00023|Arthur Conan Doyle|      A sign of four|     22.45|         1890|
| b00501|Arthur Conan Doyle|The memoirs of Sh...|     14.22|         1893|
| b01001|Arthur Conan Doyle|The adventures of...|     19.83|         1892|
+-------+------------------+--------------------+----------+-------------+

import com.datastax.spark.connector._
readBooksDF: org.apache.spark.sql.DataFrame = [book_id: string, book_author: string ... 3 more fields]
newBooksDF: org.apache.spark.sql.DataFrame = [book_id: string, book_author: string ... 3 more fields]

后续步骤