apache iceberg 查询效率_最强指南！数据湖Apache Hudi、Iceberg、Delta环境搭建

1. 引入

作为依赖Spark的三个数据湖开源框架Delta，Hudi和Iceberg，本篇文章为这三个框架准备环境，并从Apache Spark、Hive和Presto的查询角度进行比较。主要分为三部分

• 准备单节点集群，包括：Hadoop，Spark，Hive，Presto和所有依赖项。

• 测试Delta，Hudi，Iceberg在更新，删除，时间旅行，Schema合并中的行为方式。还会检查事务日志，以及默认配置和相同数据量的大小差异。

• 使用Apache Hive和Presto查询。

2. 环境准备

2.1 单节点集群

版本如下

ubuntu-18.04.3-live-server-amd64

openjdk-8-jdk

scala-2.11.12

spark-2.4.4-bin-hadoop2.7

hadoop-2.7.7

apache-hive-2.3.6-bin

presto-server-329.tar

org.apache.iceberg:iceberg-spark-runtime:0.7.0-incubating

org.apache.hudi:hudi-spark-bundle:0.5.0-incubating

io.delta:delta-core_2.11:0.5.0

在Ubuntu中，我使用的是超级用户spuser，并为该用户生成hadoop所需的授权密钥。

ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa

cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys

chmod 0600 ~/.ssh/authorized_keys

为Spark安装Java 1.8

#1.

sudo add-apt-repository ppa:openjdk-r/ppa

sudo apt-get update

sudo apt-get install openjdk-8-jdk

sudo update-alternatives --config java

sudo update-alternatives --config javac

确认版本为Java 1.8

#2.

spuser@acid:~$ java -version

openjdk version "1.8.0_232"

OpenJDK Runtime Environment (build 1.8.0_232-8u232-b09-0ubuntu1~16.04.1-b09)

OpenJDK 64-Bit Server VM (build 25.232-b09, mixed mode)

下载所有的依赖包

#3.

mkdir downloads

cd downloads/

wget https://downloads.lightbend.com/scala/2.11.12/scala-2.11.12.deb

wget http://apache.mirror.vu.lt/apache/spark/spark-2.4.4/spark-2.4.4-bin-hadoop2.7.tgz

wget http://apache.mirror.vu.lt/apache/spark/spark-3.0.0-preview2/spark-3.0.0-preview2-bin-hadoop2.7.tgz

wget https://archive.apache.org/dist/hadoop/core/hadoop-2.7.7/hadoop-2.7.7.tar.gz

wget http://apache.mirror.vu.lt/apache/hive/hive-2.3.6/apache-hive-2.3.6-bin.tar.gz

wget https://repo1.maven.org/maven2/io/prestosql/presto-cli/329/presto-cli-329-executable.jar

wget https://repo1.maven.org/maven2/io/prestosql/presto-server/329/presto-server-329.tar.gz

检查下载项

#4.

spuser@acid:~/downloads$ ll -h

安装Scala

#5.

sudo dpkg -i scala-2.11.12.deb

安装至/usr/local目录，对于特定版本，创建符号链接，以便将来进行更轻松的迁移

#6.

sudo tar -xzf apache-hive-2.3.6-bin.tar.gz -C /usr/local/

sudo tar -xzf hadoop-2.7.7.tar.gz -C /usr/local/

sudo tar -xzf spark-2.4.4-bin-hadoop2.7.tgz -C /usr/local/

sudo tar -xzf spark-3.0.0-preview2-bin-hadoop2.7.tgz -C /usr/local/

sudo tar -xzf presto-server-329.tar.gz -C /usr/local

sudo chown -R spuser /usr/local/apache-hive-2.3.6-bin/

sudo chown -R spuser /usr/local/hadoop-2.7.7/

sudo chown -R spuser /usr/local/spark-2.4.4-bin-hadoop2.7/

sudo chown -R spuser /usr/local/spark-3.0.0-preview2-bin-hadoop2.7/

sudo chown -R spuser /usr/local/presto-server-329/

cd /usr/local/

sudo ln -s /usr/local/apache-hive-2.3.6-bin/ /usr/local/hive

sudo chown -h spuser:spuser /usr/local/hive

sudo ln -s /usr/local/hadoop-2.7.7/ /usr/local/hadoop

sudo chown -h spuser:spuser /usr/local/hadoop

sudo ln -s /usr/local/spark-2.4.4-bin-hadoop2.7 /usr/local/spark

sudo chown -h spuser:spuser /usr/local/spark

sudo ln -s /usr/local/spark-3.0.0-preview2-bin-hadoop2.7 /usr/local/spark3

sudo chown -h spuser:spuser /usr/local/spark3

sudo ln -s /usr/local/presto-server-329 /usr/local/presto

sudo chown -h spuser:spuser /usr/local/presto

为日志和HDFS创建几个文件夹。在根目录下创建一些文件夹并不是最佳做法，但可起到沙盒作用

#7.

sudo mkdir /logs

sudo chown -R spuser /logs

mkdir /logs/hadoop

#Add dir for data

sudo mkdir /hadoop

sudo chown -R spuser /hadoop

mkdir -p /hadoop/hdfs/namenode

mkdir -p /hadoop/hdfs/datanode

#create tmp hadoop dir:

mkdir -p /tmp/hadoop

更新环境变量，.bashrc

#8.

sudo nano ~/.bashrc

#Add entries in existing file:

export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64

export PATH=$PATH:$JAVA_HOME/bin

export HADOOP_HOME=/usr/local/hadoop

export HIVE_HOME=/usr/local/hive

export PATH=$PATH:$HADOOP_HOME/bin

export PATH=$PATH:$HADOOP_HOME/sbin

export PATH=$PATH:$HIVE_HOME/bin

export HADOOP_MAPRED_HOME=$HADOOP_HOME

export HADOOP_COMMON_HOME=$HADOOP_HOME

export HADOOP_HDFS_HOME=$HADOOP_HOME

export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop

export YARN_HOME=$HADOOP_HOME

export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native

export HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib/native"

export HADOOP_LOG_DIR=/logs/hadoop

export SPARK_HOME=/usr/local/spark

export PATH=$PATH:$SPARK_HOME/bin

#Save it!

#Source it:

source ~/.bashrc

2.2 Hadoop配置

更改Hadoop配置，切换至目录

#9.

cd /usr/local/hadoop/etc/hadoop

hadoop-env.sh

#10.

#Comment existing JAVA_HOME and add new one:

export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64

core-site.xml

#11.

hadoop.tmp.dir

/tmp/hadoop

A base for other temporary directories.

fs.defaultFS

hdfs://localhost:9000

mapred-site.xml

#12.

mapreduce.framework.name

yarn

hdfs-site.xml

#13.

dfs.replication

1

dfs.namenode.name.dir

file:/hadoop/hdfs/namenode

dfs.datanode.data.dir

file:/hadoop/hdfs/datanode

yarn-site.xml

#14.

yarn.nodemanager.aux-services

mapreduce_shuffle

准备好HDFS之后，格式化并启动服务

#15.

hdfs namenode -format

start-all.sh

检查运行情况

#16.

spuser@acid:/usr/local/hadoop/etc/hadoop$ jps

9890 DataNode

10275 ResourceManager

10115 SecondaryNameNode

10613 NodeManager

9705 NameNode

10732 Jps

2.3 Hive配置

为Hive创建Hdfs目录

#17.

#Create HDFS dirs:

hdfs dfs -mkdir -p /user/hive/warehouse

hdfs dfs -mkdir /tmp

hdfs dfs -chmod g+w /user/hive/warehouse

hdfs dfs -chmod g+w /tmp

切换至Hive conf目录

#18.

cd /usr/local/hive/conf

hive-site.xml

#19.

javax.jdo.option.ConnectionURL

jdbc:derby:;databaseName=/usr/local/hive/metastore_db;create=true

JDBC connect string for a JDBC metastore.

To use SSL to encrypt/authenticate the connection, provide database-specific SSL flag in the connection URL.

For example, jdbc:postgresql://myhost/db?ssl=true for postgres database.

hive.metastore.warehouse.dir

/user/hive/warehouse

location of default database for the warehouse

hive.metastore.uris

Thrift URI for the remote metastore. Used by metastore client to connect to remote metastore.

javax.jdo.option.ConnectionDriverName

org.apache.derby.jdbc.EmbeddedDriver

Driver class name for a JDBC metastore

javax.jdo.PersistenceManagerFactoryClass

org.datanucleus.api.jdo.JDOPersistenceManagerFactory

class implementing the jdo persistence

hive.metastore.schema.verification

false

hive-env.sh

#20.

# The heap size of the jvm stared by hive shell script can be controlled via:

#

export HADOOP_HEAPSIZE=512

#

# Larger heap size may be required when running queries over large number of files or partitions.

# By default hive shell scripts use a heap size of 256 (MB). Larger heap size would also be

# appropriate for hive server (hwi etc).

# Set HADOOP_HOME to point to a specific hadoop install directory

export HADOOP_HOME=/usr/local/hadoop

# Hive Configuration Directory can be controlled by:

export HIVE_CONF_DIR=/usr/local/hive/conf

# Folder containing extra ibraries required for hive compilation/execution can be controlled by:

export HIVE_AUX_JARS_PATH=/usr/local/hive/lib/*.jar

在创建Hive metastore之前请更新hive-schema-2.3.0.derby.sql，否则iceberg将无法创建表，会有如下错误

#21.

ERROR metastore.RetryingHMSHandler: Retrying HMSHandler after 2000 ms (attempt 8 of 10) with error: javax.jdo.JDODataStoreException: Insert of object "org.apache.hadoop.hive.metastore.model.MTable@604201a0" using statement "INSERT INTO TBLS (TBL_ID,OWNER,CREATE_TIME,SD_ID,TBL_NAME,VIEW_EXPANDED_TEXT,LAST_ACCESS_TIME,DB_ID,RETENTION,VIEW_ORIGINAL_TEXT,TBL_TYPE) VALUES (?,?,?,?,?,?,?,?,?,?,?)" failed : Column 'IS_REWRITE_ENABLED' cannot accept a NULL value.

更新hive-schema-2.3.0.derby.sql

#22.

nano /usr/local/hive/scripts/metastore/upgrade/derby/hive-schema-2.3.0.derby.sql

#update statement: "APP"."TBLS"

CREATE TABLE "APP"."TBLS" ("TBL_ID" BIGINT NOT NULL, "CREATE_TIME" INTEGER NOT NULL, "DB_ID" BIGINT, "LAST_ACCESS_TIME" INTEGER NOT NULL, "OWNER" VARCHAR(767), "RETENTION" INTEGER NOT NULL, "SD_ID" BIGINT, "TBL_NAME" VARCHAR(256), "TBL_TYPE" VARCHAR(128), "VIEW_EXPANDED_TEXT" LONG VARCHAR, "VIEW_ORIGINAL_TEXT" LONG VARCHAR, "IS_REWRITE_ENABLED" CHAR(1) NOT NULL DEFAULT 'N');

更新后创建Hive metastore

#23.

schematool -initSchema -dbType derby --verbose

检查schema是否创建成功

#24.

...

beeline> Initialization script completed

schemaTool completed

通过CLI创建Hive

#25.

hive -e "show databases"

2.4 Presto配置

创建config目录

#26.

mkdir -p /usr/local/presto/etc

创建配置文件 /usr/local/presto/etc/config.properties

#27.

coordinator=true

node-scheduler.include-coordinator=true

http-server.http.port=8080

query.max-memory=5GB

query.max-memory-per-node=1GB

query.max-total-memory-per-node=2GB

discovery-server.enabled=true

discovery.uri=http://localhost:8080

创建JVM配置文件/usr/local/presto/etc/jvm.properties

#28.

-server

-Xmx16G

-XX:+UseG1GC

-XX:G1HeapRegionSize=32M

-XX:+UseGCOverheadLimit

-XX:+ExplicitGCInvokesConcurrent

-XX:+HeapDumpOnOutOfMemoryError

-XX:+ExitOnOutOfMemoryError

创建节点配置文件 /usr/local/presto/etc/node.properties

#29.

node.environment=production

node.id=ffffffff-ffff-ffff-ffff-ffffffffffff

node.data-dir=/var/presto/data

创建相关目录

#30.

sudo mkdir -p /var/presto/data

sudo chown spuser:spuser -h /var/presto

sudo chown spuser:spuser -h /var/presto/data

创建catalog和hive配置文件 /usr/local/presto/etc/catalog/hive.properties

#31.

connector.name=hive-hadoop2

hive.metastore.uri=thrift://localhost:9083

2.5 Spark相关配置

检查scala版本

#32.

scala -version

#make sure that you can see something like:

Scala code runner version 2.11.12 -- Copyright 2002-2017, LAMP/EPFL

#otherwise get back to step #5.

切换至Spark conf目录

#33.

cd /usr/local/spark/conf

spark-env.sh

#34.

#add

export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop

export SPARK_CONF_DIR=/usr/local/spark/conf

export SPARK_LOCAL_IP=127.0.0.1

拷贝hive-site.xml，以便使用Hive和Presto测试delta，hudl，iceberg行为

#35.

cp /usr/local/hive/conf/hive-site.xml /usr/local/spark/conf/

下载所有的依赖

#36.

spark-shell --packages org.apache.iceberg:iceberg-spark-runtime:0.7.0-incubating,org.apache.hudi:hudi-spark-bundle:0.5.0-incubating,io.delta:delta-core_2.11:0.5.0 --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'

2.6 测试三个框架

Delta

#37.

import org.apache.spark.sql.SaveMode._

spark.range(1000).toDF.write.format("delta").mode(Overwrite).save("/tmp/delta_tab01")

Hudi

#38.

import org.apache.spark.sql.SaveMode._

import org.apache.hudi.DataSourceWriteOptions._

import org.apache.hudi.config.HoodieWriteConfig._

spark.range(1000).write.format("org.apache.hudi").option(TABLE_NAME, "hudi_tab01").option(PRECOMBINE_FIELD_OPT_KEY, "id").option(RECORDKEY_FIELD_OPT_KEY, "id").mode(Overwrite).save("/tmp/hudi_tab01")

Iceberg

#39.

import org.apache.iceberg.hive.HiveCatalog

import org.apache.iceberg.catalog._

import org.apache.iceberg.Schema

import org.apache.iceberg.types.Types._

import org.apache.iceberg.PartitionSpec

import org.apache.iceberg.spark.SparkSchemaUtil

import org.apache.iceberg.hadoop.HadoopTables

val name = TableIdentifier.of("default","iceberg_tab01");

val df1=spark.range(1000).toDF.withColumn("level",lit("1"))

val df1_schema = SparkSchemaUtil.convert(df1.schema)

val partition_spec=PartitionSpec.builderFor(df1_schema).identity("level").build

val tables = new HadoopTables(spark.sessionState.newHadoopConf())

val table = tables.create(df1_schema, partition_spec, "hdfs:/tmp/iceberg_tab01")

df1.write.format("iceberg").mode("append").save("hdfs:/tmp/iceberg_tab01")

检查HDFS上结果

#40.

hdfs dfs -ls -h -R /tmp/delta* && hdfs dfs -ls -h -R /tmp/hudi* && hdfs dfs -ls -h -R /tmp/iceberg*

3. 总结

本篇文章展示了如何搭建测试三个数据湖环境所依赖的所有环境，以及进行了简单的测试，希望这对你有用。