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Spark使用OSS Select加速数据查询

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本文介绍如何配置Spark使用OSS Select加速数据查询，以及使用OSS Select查询数据的优势。

背景信息

本文所有操作基于Apache Impala(CDH6) 处理OSS数据搭建的CDH6集群及配置。

说明

文中所有${}的内容为环境变量，请根据您实际的环境修改。

步骤一：配置Spark支持读写OSS

由于Spark默认没有将OSS的支持包放到它的CLASSPATH里面，所以我们需要配置Spark支持读写OSS。您需要在所有的CDH节点执行以下操作：

  1. 进入到${CDH_HOME}/lib/spark目录，执行如下命令： 

    [root@cdh-master spark]# cd jars/
[root@cdh-master jars]# ln -s ../../../jars/hadoop-aliyun-3.0.0-cdh6.0.1.jar hadoop-aliyun.jar
[root@cdh-master jars]# ln -s ../../../jars/aliyun-sdk-oss-2.8.3.jar aliyun-sdk-oss-2.8.3.jar
[root@cdh-master jars]# ln -s ../../../jars/jdom-1.1.jar jdom-1.1.jar

  2. 进入${CDH_HOME}/lib/spark目录执行查询。 

    [root@cdh-master spark]# ./bin/spark-shell
WARNING: User-defined SPARK_HOME (/opt/cloudera/parcels/CDH-6.0.1-1.cdh6.0.1.p0.590678/lib/spark) overrides detected (/opt/cloudera/parcels/CDH/lib/spark).
WARNING: Running spark-class from user-defined location.
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context Web UI available at http://x.x.x.x:4040
Spark context available as 'sc' (master = yarn, app id = application_1540878848110_0004).
Spark session available as'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.2.0-cdh6.0.1
      /_/

Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_152)
Type in expressions to have them evaluated.
Type :help for more information.

scala> val myfile = sc.textFile("oss://{your-bucket-name}/50/store_sales")
myfile: org.apache.spark.rdd.RDD[String] = oss://{your-bucket-name}/50/store_sales MapPartitionsRDD[1] at textFile at <console>:24

scala> myfile.count()
res0: Long = 144004764

scala> myfile.map(line => line.split('|')).filter(_(0).toInt >= 2451262).take(3)
res15: Array[Array[String]] = Array(Array(2451262, 71079, 20359, 154660, 284233, 6206, 150579, 46, 512, 2160001, 84, 6.94, 11.38, 9.33, 681.83, 783.72, 582.96, 955.92, 5.09, 681.83, 101.89, 106.98, -481.07), Array(2451262, 71079, 26863, 154660, 284233, 6206, 150579, 46, 345, 2160001, 12, 67.82, 115.29, 25.36, 0.00, 304.32, 813.84, 1383.48, 21.30, 0.00, 304.32, 325.62, -509.52), Array(2451262, 71079, 55852, 154660, 284233, 6206, 150579, 46, 243, 2160001, 74, 32.41, 34.67, 1.38, 0.00, 102.12, 2398.34, 2565.58, 4.08, 0.00, 102.12, 106.20, -2296.22))

scala> myfile.map(line => line.split('|')).filter(_(0) >= "2451262").saveAsTextFile("oss://{your-bucket-name}/spark-oss-test.1")

    可正常执行查询，则部署生效。

步骤二：配置Spark支持OSS Select

OSS Select详情请参见OSS Select，以下内容基于oss-cn-shenzhen.aliyuncs.com这个OSS EndPoint来进行。您需要在所有的CDH节点执行以下操作：

  1. 下载 OSS Select的Spark支持包到${CDH_HOME}/jars目录下（目前该支持包还在测试中）。

  2. 解压支持包。

    [root@cdh-master jars]# tar -tvf spark-2.2.0-oss-select-0.1.0-SNAPSHOT.tar.gz 
drwxr-xr-x root/root 0 2018-10-30 17:59 spark-2.2.0-oss-select-0.1.0-SNAPSHOT/
-rw-r--r-- root/root 26514 2018-10-30 16:11 spark-2.2.0-oss-select-0.1.0-SNAPSHOT/stax-api-1.0.1.jar
-rw-r--r-- root/root 547584 2018-10-30 16:11 spark-2.2.0-oss-select-0.1.0-SNAPSHOT/aliyun-sdk-oss-3.3.0.jar
-rw-r--r-- root/root 13277 2018-10-30 16:11 spark-2.2.0-oss-select-0.1.0-SNAPSHOT/aliyun-java-sdk-sts-3.0.0.jar
-rw-r--r-- root/root 116337 2018-10-30 16:11 spark-2.2.0-oss-select-0.1.0-SNAPSHOT/aliyun-java-sdk-core-3.4.0.jar
-rw-r--r-- root/root 215492 2018-10-30 16:11 spark-2.2.0-oss-select-0.1.0-SNAPSHOT/aliyun-java-sdk-ram-3.0.0.jar
-rw-r--r-- root/root 67758 2018-10-30 16:11 spark-2.2.0-oss-select-0.1.0-SNAPSHOT/jettison-1.1.jar
-rw-r--r-- root/root 57264 2018-10-30 16:11 spark-2.2.0-oss-select-0.1.0-SNAPSHOT/json-20170516.jar
-rw-r--r-- root/root 890168 2018-10-30 16:11 spark-2.2.0-oss-select-0.1.0-SNAPSHOT/jaxb-impl-2.2.3-1.jar
-rw-r--r-- root/root 458739 2018-10-30 16:11 spark-2.2.0-oss-select-0.1.0-SNAPSHOT/jersey-core-1.9.jar
-rw-r--r-- root/root 147952 2018-10-30 16:11 spark-2.2.0-oss-select-0.1.0-SNAPSHOT/jersey-json-1.9.jar
-rw-r--r-- root/root 788137 2018-10-30 16:11 spark-2.2.0-oss-select-0.1.0-SNAPSHOT/aliyun-java-sdk-ecs-4.2.0.jar
-rw-r--r-- root/root 153115 2018-10-30 16:11 spark-2.2.0-oss-select-0.1.0-SNAPSHOT/jdom-1.1.jar
-rw-r--r-- root/root 65437 2018-10-31 14:41 spark-2.2.0-oss-select-0.1.0-SNAPSHOT/aliyun-oss-select-spark_2.11-0.1.0-SNAPSHOT.jar

  3. 进入${CDH_HOME}/lib/spark/jars目录，执行如下命令： 

    [root@cdh-master jars]# pwd
/opt/cloudera/parcels/CDH/lib/spark/jars
[root@cdh-master jars]# rm -f aliyun-sdk-oss-2.8.3.jar
[root@cdh-master jars]# ln -s ../../../jars/aliyun-oss-select-spark_2.11-0.1.0-SNAPSHOT.jar aliyun-oss-select-spark_2.11-0.1.0-SNAPSHOT.jar
[root@cdh-master jars]# ln -s ../../../jars/aliyun-java-sdk-core-3.4.0.jar aliyun-java-sdk-core-3.4.0.jar
[root@cdh-master jars]# ln -s ../../../jars/aliyun-java-sdk-ecs-4.2.0.jar aliyun-java-sdk-ecs-4.2.0.jar
[root@cdh-master jars]# ln -s ../../../jars/aliyun-java-sdk-ram-3.0.0.jar aliyun-java-sdk-ram-3.0.0.jar
[root@cdh-master jars]# ln -s ../../../jars/aliyun-java-sdk-sts-3.0.0.jar aliyun-java-sdk-sts-3.0.0.jar
[root@cdh-master jars]# ln -s ../../../jars/aliyun-sdk-oss-3.3.0.jar aliyun-sdk-oss-3.3.0.jar
[root@cdh-master jars]# ln -s ../../../jars/jdom-1.1.jar jdom-1.1.jar

对比测试

测试环境：使用spark on yarn进行对比测试，其中Node Manager节点是4个，每个节点最多可以运行4个container，每个container配备的资源是1核2GB内存。

测试数据：共630MB，包含3列，分别是姓名、公司和年龄。

ot@cdh-master jars]# hadoop fs -ls oss://select-test-sz/people/
Found 10 items
-rw-rw-rw-   1   63079930 2018-10-30 17:03 oss://select-test-sz/people/part-00000
-rw-rw-rw-   1   63079930 2018-10-30 17:03 oss://select-test-sz/people/part-00001
-rw-rw-rw-   1   63079930 2018-10-30 17:05 oss://select-test-sz/people/part-00002
-rw-rw-rw-   1   63079930 2018-10-30 17:05 oss://select-test-sz/people/part-00003
-rw-rw-rw-   1   63079930 2018-10-30 17:06 oss://select-test-sz/people/part-00004
-rw-rw-rw-   1   63079930 2018-10-30 17:12 oss://select-test-sz/people/part-00005
-rw-rw-rw-   1   63079930 2018-10-30 17:14 oss://select-test-sz/people/part-00006
-rw-rw-rw-   1   63079930 2018-10-30 17:14 oss://select-test-sz/people/part-00007
-rw-rw-rw-   1   63079930 2018-10-30 17:15 oss://select-test-sz/people/part-00008
-rw-rw-rw-   1   63079930 2018-10-30 17:16 oss://select-test-sz/people/part-00009

进入到${CDH_HOME}/lib/spark/，启动spark-shell ，分别测试使用OSS Select查询数据和不使用OSS Select查询数据： 

[root@cdh-master spark]# ./bin/spark-shell
WARNING: User-defined SPARK_HOME (/opt/cloudera/parcels/CDH-6.0.1-1.cdh6.0.1.p0.590678/lib/spark) overrides detected (/opt/cloudera/parcels/CDH/lib/spark).
WARNING: Running spark-class from user-defined location.
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context Web UI available at http://x.x.x.x:4040
Spark context available as 'sc' (master = yarn, app id = application_1540887123331_0008).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.2.0-cdh6.0.1
      /_/

Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_152)
Type in expressions to have them evaluated.
Type :help for more information.

scala> val sqlContext = spark.sqlContext
sqlContext: org.apache.spark.sql.SQLContext = org.apache.spark.sql.SQLContext@4bdef487

scala> sqlContext.sql("CREATE TEMPORARY VIEW people USING com.aliyun.oss " +
     |   "OPTIONS (" +
     |   "oss.bucket 'select-test-sz', " +
     |   "oss.prefix 'people', " + // objects with this prefix belong to this table
     |   "oss.schema 'name string, company string, age long'," + // like 'column_a long, column_b string'
     |   "oss.data.format 'csv'," + // we only support csv now
     |   "oss.input.csv.header 'None'," +
     |   "oss.input.csv.recordDelimiter '\r\n'," +
     |   "oss.input.csv.fieldDelimiter ','," +
     |   "oss.input.csv.commentChar '#'," +
     |   "oss.input.csv.quoteChar '\"'," +
     |   "oss.output.csv.recordDelimiter '\n'," +
     |   "oss.output.csv.fieldDelimiter ','," +
     |   "oss.output.csv.quoteChar '\"'," +
     |   "oss.endpoint 'oss-cn-shenzhen.aliyuncs.com', " +
     |   "oss.accessKeyId 'Your Access Key Id', " +
     |   "oss.accessKeySecret 'Your Access Key Secret')")
res0: org.apache.spark.sql.DataFrame = []

scala>   val sql: String = "select count(*) from people where name like 'Lora%'"
sql: String = select count(*) from people where name like 'Lora%'

scala>   sqlContext.sql(sql).show()
+--------+
|count(1)|
+--------+
|   31770|
+--------+

scala> val textFile = sc.textFile("oss://select-test-sz/people/")
textFile: org.apache.spark.rdd.RDD[String] = oss://select-test-sz/people/ MapPartitionsRDD[8] at textFile at <console>:24

scala> textFile.map(line => line.split(',')).filter(_(0).startsWith("Lora")).count()
res3: Long = 31770

从下图可看到：使用OSS Select查询数据耗时为15s，不使用OSS Select查询数据耗时为54s，使用OSS Select能大幅度加快查询速度。

Spark对接OSS Select支持包的实现（Preview）

通过扩展Spark的 DataSource API 可以实现Spark对接OSS Select。通过实现PrunedFilteredScan，可以把需要的列和过滤条件下推到OSS Select执行。目前这个支持包还在开发中，定义的规范和支持的过滤条件如下：

  • 定义的规范： 

    scala> sqlContext.sql("CREATE TEMPORARY VIEW people USING com.aliyun.oss " +
     |   "OPTIONS (" +
     |   "oss.bucket 'select-test-sz', " +
     |   "oss.prefix 'people', " + // objects with this prefix belong to this table
     |   "oss.schema 'name string, company string, age long'," + // like 'column_a long, column_b string'
     |   "oss.data.format 'csv'," + // we only support csv now
     |   "oss.input.csv.header 'None'," +
     |   "oss.input.csv.recordDelimiter '\r\n'," +
     |   "oss.input.csv.fieldDelimiter ','," +
     |   "oss.input.csv.commentChar '#'," +
     |   "oss.input.csv.quoteChar '\"'," +
     |   "oss.output.csv.recordDelimiter '\n'," +
     |   "oss.output.csv.fieldDelimiter ','," +
     |   "oss.output.csv.quoteChar '\"'," +
     |   "oss.endpoint 'oss-cn-shenzhen.aliyuncs.com', " +
     |   "oss.accessKeyId 'Your Access Key Id', " +
     |   "oss.accessKeySecret 'Your Access Key Secret')")

    字段

    说明

    oss.bucket

    数据所在的Bucket。

    oss.prefix

    拥有这个前缀的Object都属于定义的这个TEMPORARY VIEW。

    oss.schema

    这个TEMPORARY VIEW的schema，目前通过字符串指定，后续会通过一个文件来指定schema。

    oss.data.format

    数据内容的格式，目前支持CSV格式，其他格式也会陆续支持。

    oss.input.csv.*

    定义CSV输入格式参数。

    oss.output.csv.*

    定义CSV输出格式参数。

    oss.endpoint

    Bucket所在的Endpoint。

    oss.accessKeyId

    填写AccessKeyId。

    oss.accessKeySecret

    填写AccessKeySecret。

    说明

    目前只定义了基本参数，详情请参见 SelectObject。

  • 支持的过滤条件：=,<,>,<=, >=,||,or,not,and,in,like(StringStartsWith,StringEndsWith,StringContains)。对于不能下推的过滤条件，例如算术运算、字符串拼接等通过PrunedFilteredScan获取不到的条件，则只下推需要的列到OSS Select。

    说明

    OSS Select还支持其他过滤条件，详情请参见SelectObject。

对比TPC-H的查询

通过测试TPC-H中query1.sql对于lineitem这个table的查询性能，来检验配置效果。为了能使OSS Select过滤更多的数据，我们将where条件由l_shipdate <= '1998-09-16'改为where l_shipdate > '1997-09-16'，测试数据大小为2.27 GB。仅使用Spark SQL查询和在Spark SQL上使用OSS Select查询的方式如下：

  • 仅使用Spark SQL查询 

    [root@cdh-master ~]# hadoop fs -ls oss://select-test-sz/data/lineitem.csv
-rw-rw-rw-   1 2441079322 2018-10-31 11:18 oss://select-test-sz/data/lineitem.csv

  • 在Spark SQL上使用OSS Select查询 

    scala> import org.apache.spark.sql.types.{IntegerType, LongType, StringType, StructField, StructType, DoubleType}
import org.apache.spark.sql.types.{IntegerType, LongType, StringType, StructField, StructType, DoubleType}

scala> import org.apache.spark.sql.{Row, SQLContext}
import org.apache.spark.sql.{Row, SQLContext}

scala> val sqlContext = spark.sqlContext
sqlContext: org.apache.spark.sql.SQLContext = org.apache.spark.sql.SQLContext@74e2cfc5

scala> val textFile = sc.textFile("oss://select-test-sz/data/lineitem.csv")
textFile: org.apache.spark.rdd.RDD[String] = oss://select-test-sz/data/lineitem.csv MapPartitionsRDD[1] at textFile at <console>:26

scala> val dataRdd = textFile.map(_.split('|'))
dataRdd: org.apache.spark.rdd.RDD[Array[String]] = MapPartitionsRDD[2] at map at <console>:28

scala> val schema = StructType(
     |     List(
     |         StructField("L_ORDERKEY",LongType,true),
     |         StructField("L_PARTKEY",LongType,true),
     |         StructField("L_SUPPKEY",LongType,true),
     |         StructField("L_LINENUMBER",IntegerType,true),
     |         StructField("L_QUANTITY",DoubleType,true),
     |         StructField("L_EXTENDEDPRICE",DoubleType,true),
     |         StructField("L_DISCOUNT",DoubleType,true),
     |         StructField("L_TAX",DoubleType,true),
     |         StructField("L_RETURNFLAG",StringType,true),
     |         StructField("L_LINESTATUS",StringType,true),
     |         StructField("L_SHIPDATE",StringType,true),
     |         StructField("L_COMMITDATE",StringType,true),
     |         StructField("L_RECEIPTDATE",StringType,true),
     |         StructField("L_SHIPINSTRUCT",StringType,true),
     |         StructField("L_SHIPMODE",StringType,true),
     |         StructField("L_COMMENT",StringType,true)
     |     )
     | )
schema: org.apache.spark.sql.types.StructType = StructType(StructField(L_ORDERKEY,LongType,true), StructField(L_PARTKEY,LongType,true), StructField(L_SUPPKEY,LongType,true), StructField(L_LINENUMBER,IntegerType,true), StructField(L_QUANTITY,DoubleType,true), StructField(L_EXTENDEDPRICE,DoubleType,true), StructField(L_DISCOUNT,DoubleType,true), StructField(L_TAX,DoubleType,true), StructField(L_RETURNFLAG,StringType,true), StructField(L_LINESTATUS,StringType,true), StructField(L_SHIPDATE,StringType,true), StructField(L_COMMITDATE,StringType,true), StructField(L_RECEIPTDATE,StringType,true), StructField(L_SHIPINSTRUCT,StringType,true), StructField(L_SHIPMODE,StringType,true), StructField(L_COMMENT,StringType,true))

scala> val dataRowRdd = dataRdd.map(p => Row(p(0).toLong, p(1).toLong, p(2).toLong, p(3).toInt, p(4).toDouble, p(5).toDouble, p(6).toDouble, p(7).toDouble, p(8), p(9), p(10), p(11), p(12), p(13), p(14), p(15)))
dataRowRdd: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = MapPartitionsRDD[3] at map at <console>:30

scala> val dataFrame = sqlContext.createDataFrame(dataRowRdd, schema)
dataFrame: org.apache.spark.sql.DataFrame = [L_ORDERKEY: bigint, L_PARTKEY: bigint ... 14 more fields]

scala> dataFrame.createOrReplaceTempView("lineitem")

scala> spark.sql("select l_returnflag, l_linestatus, sum(l_quantity) as sum_qty, sum(l_extendedprice) as sum_base_price, sum(l_extendedprice * (1 - l_discount)) as sum_disc_price, sum(l_extendedprice * (1 - l_discount) * (1 + l_tax)) as sum_charge, avg(l_quantity) as avg_qty, avg(l_extendedprice) as avg_price, avg(l_discount) as avg_disc, count(*) as count_order from lineitem where l_shipdate > '1997-09-16' group by l_returnflag, l_linestatus order by l_returnflag, l_linestatus").show()
+------------+------------+-----------+--------------------+--------------------+--------------------+------------------+------------------+-------------------+-----------+
|l_returnflag|l_linestatus|    sum_qty|      sum_base_price|      sum_disc_price|          sum_charge|           avg_qty|         avg_price|           avg_disc|count_order|
+------------+------------+-----------+--------------------+--------------------+--------------------+------------------+------------------+-------------------+-----------+
|           N|           O|7.5697385E7|1.135107538838699...|1.078345555027154...|1.121504616321447...|25.501957856643052|38241.036487881756|0.04999335309103123|    2968297|
+------------+------------+-----------+--------------------+--------------------+--------------------+------------------+------------------+-------------------+-----------+

scala> sqlContext.sql("CREATE TEMPORARY VIEW item USING com.aliyun.oss " +
     |   "OPTIONS (" +
     |   "oss.bucket 'select-test-sz', " +
     |   "oss.prefix 'data', " +
     |   "oss.schema 'L_ORDERKEY long, L_PARTKEY long, L_SUPPKEY long, L_LINENUMBER int, L_QUANTITY double, L_EXTENDEDPRICE double, L_DISCOUNT double, L_TAX double, L_RETURNFLAG string, L_LINESTATUS string, L_SHIPDATE string, L_COMMITDATE string, L_RECEIPTDATE string, L_SHIPINSTRUCT string, L_SHIPMODE string, L_COMMENT string'," +
     |   "oss.data.format 'csv'," + // we only support csv now
     |   "oss.input.csv.header 'None'," +
     |   "oss.input.csv.recordDelimiter '\n'," +
     |   "oss.input.csv.fieldDelimiter '|'," +
     |   "oss.input.csv.commentChar '#'," +
     |   "oss.input.csv.quoteChar '\"'," +
     |   "oss.output.csv.recordDelimiter '\n'," +
     |   "oss.output.csv.fieldDelimiter ','," +
     |   "oss.output.csv.commentChar '#'," +
     |   "oss.output.csv.quoteChar '\"'," +
     |   "oss.endpoint 'oss-cn-shenzhen.aliyuncs.com', " +
     |   "oss.accessKeyId 'Your Access Key Id', " +
     |   "oss.accessKeySecret 'Your Access Key Secret')")
res2: org.apache.spark.sql.DataFrame = []

scala> sqlContext.sql("select l_returnflag, l_linestatus, sum(l_quantity) as sum_qty, sum(l_extendedprice) as sum_base_price, sum(l_extendedprice * (1 - l_discount)) as sum_disc_price, sum(l_extendedprice * (1 - l_discount) * (1 + l_tax)) as sum_charge, avg(l_quantity) as avg_qty, avg(l_extendedprice) as avg_price, avg(l_discount) as avg_disc, count(*) as count_order from item where l_shipdate > '1997-09-16' group by l_returnflag, l_linestatus order by l_returnflag, l_linestatus").show()

scala> sqlContext.sql("select l_returnflag, l_linestatus, sum(l_quantity) as sum_qty, sum(l_extendedprice) as sum_base_price, sum(l_extendedprice * (1 - l_discount)) as sum_disc_price, sum(l_extendedprice * (1 - l_discount) * (1 + l_tax)) as sum_charge, avg(l_quantity) as avg_qty, avg(l_extendedprice) as avg_price, avg(l_discount) as avg_disc, count(*) as count_order from item where l_shipdate > '1997-09-16' group by l_returnflag, l_linestatus order by l_returnflag, l_linestatus").show()
+------------+------------+-----------+--------------------+--------------------+--------------------+------------------+-----------------+-------------------+-----------+
|l_returnflag|l_linestatus|    sum_qty|      sum_base_price|      sum_disc_price|          sum_charge|           avg_qty|        avg_price|           avg_disc|count_order|
+------------+------------+-----------+--------------------+--------------------+--------------------+------------------+-----------------+-------------------+-----------+
|           N|           O|7.5697385E7|1.135107538838701E11|1.078345555027154...|1.121504616321447...|25.501957856643052|38241.03648788181|0.04999335309103024|    2968297|
+------------+------------+-----------+--------------------+--------------------+--------------------+------------------+-----------------+-------------------+-----------+

从下图可以看出：在Spark SQL上使用OSS Select查询数据耗时为38s，在Spark SQL上不使用OSS Select查询数据耗时为2.5min，使用OSS Select可大幅度加快查询速度。

参考文档

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