Recently Spring Boot has released version 2.0.0.RELEASE, so I did a small demo which included the basic CRUD, I have uploaded the code to github.
There are two branches, master is the normal branch, and docker branch will create docker image when you build.
Days before I have installed presto and ambari separately, officially ambari doesn’t support presto, you have to download ambari-presto-service and configure it yourself if you wanna manage presto on ambari.
So I tried this.
1.download hdp yum repository
wget -nv http://public-repo-1.hortonworks.com/HDP/centos6/2.x/updates/2.6.3.0/hdp.repo -O /etc/yum.repos.d/HDP.repo
2.download ambari-presto-service and configure
version=`hdp-select status hadoop-client | sed 's/hadoop-client - ([0-9].[0-9]).*/1/'`
mkdir /var/lib/ambari-server/resources/stacks/HDP/$version/services/PRESTO
wget https://github.com/prestodb/ambari-presto-service/releases/download/v1.2/ambari-presto-1.2.tar.gz
tar -xvf ambari-presto-1.2.tar.gz -C /var/lib/ambari-server/resources/stacks/HDP/$version/services/PRESTO
mv /var/lib/ambari-server/resources/stacks/HDP/$version/services/PRESTO/ambari-presto-1.2/* /var/lib/ambari-server/resources/stacks/HDP/$version/services/PRESTO
rm -rf /var/lib/ambari-server/resources/stacks/HDP/$version/services/PRESTO/ambari-presto-1.2
chmod -R +x /var/lib/ambari-server/resources/stacks/HDP/$version/services/PRESTO/*
3.restart ambari-server
ambari-server restart
4.add presto service on ambari, please configure discovery.uri when you add presto service, e.g. discovery.uri: http://coordinator:8285
After doing this, you could add catalogs and use presto as query engine.
I did a simple query comparison between Tez and Presto, if you wanna accurate benchmark result, I think this benchmark test could help. The query is to calculate sum on a hive table.
Presto: 4s
presto:test> select sum(count) as sum from (
-> select count(*) as count from t0004998 where month = '6.5'
-> union
-> select count(*) as count from t0004998 where typestatus in ('VL2216','VL2217','VL2218','VL2219','VL2220')
-> union
-> select count(*) as count from t0004998 where countrycode in ('FAMILY','FORM','GENUS','KINGDOM','ORDER','PHYLUM','SPECIES')
-> ) t;
sum
--------
307374
(1 row)
Query 20180317_102034_00040_sq83e, FINISHED, 1 node
Splits: 29 total, 29 done (100.00%)
0:04 [982K rows, 374MB] [231K rows/s, 87.8MB/s]
Tez: 29.77s
hive> select sum(count) from (
> select count(*) as count from t0004998 where month = "6.5"
> union
> select count(*) as count from t0004998 where typestatus in ("VL2216","VL2217","VL2218","VL2219","VL2220")
> union
> select count(*) as count from t0004998 where countrycode in ("FAMILY","FORM","GENUS","KINGDOM","ORDER","PHYLUM","SPECIES")
> ) t;
Query ID = hdfs_20180317102109_5fd30986-f840-450e-aedd-b51c5e3a48f1
Total jobs = 1
Launching Job 1 out of 1
Status: Running (Executing on YARN cluster with App id application_1521267007048_0012)
--------------------------------------------------------------------------------
VERTICES STATUS TOTAL COMPLETED RUNNING PENDING FAILED KILLED
--------------------------------------------------------------------------------
Map 1 .......... SUCCEEDED 1 1 0 0 0 0
Map 10 ......... SUCCEEDED 1 1 0 0 1 0
Map 8 .......... SUCCEEDED 1 1 0 0 0 0
Reducer 11 ..... SUCCEEDED 1 1 0 0 0 0
Reducer 2 ...... SUCCEEDED 1 1 0 0 0 1
Reducer 4 ...... SUCCEEDED 1 1 0 0 0 0
Reducer 6 ...... SUCCEEDED 1 1 0 0 0 0
Reducer 7 ...... SUCCEEDED 1 1 0 0 0 0
Reducer 9 ...... SUCCEEDED 1 1 0 0 0 0
--------------------------------------------------------------------------------
VERTICES: 09/09 [==========================>>] 100% ELAPSED TIME: 29.77 s
--------------------------------------------------------------------------------
OK
307374
Time taken: 30.732 seconds, Fetched: 1 row(s)
emmm, not bad..
Presto is a distributed query engine which is developed by Facebook, for specific concept and advantages, please refer to the official document, below are the steps how I build pseudo cluster on my mac.
1.download presto
wget https://repo1.maven.org/maven2/com/facebook/presto/presto-server/0.196/presto-server-0.196.tar.gz
tar -zvxf presto-server-0.196.tar.gz && cd presto-server-0.196
2.configure configurations
mkdir etc
cat << 'EOF' > etc/jvm.config
-server
-Xmx16G
-Xms16G
-XX:+UseG1GC
-XX:G1HeapRegionSize=32M
-XX:+UseGCOverheadLimit
-XX:+ExplicitGCInvokesConcurrent
-XX:+HeapDumpOnOutOfMemoryError
-XX:+ExitOnOutOfMemoryError
EOF
cat << 'EOF' > etc/log.properties
com.facebook.presto=INFO
EOF
cat << 'EOF' > etc/config1.properties
coordinator=true
node-scheduler.include-coordinator=true
http-server.http.port=8001
query.max-memory=24GB
query.max-memory-per-node=8GB
discovery-server.enabled=true
discovery.uri=http://localhost:8001
EOF
cat << 'EOF' > etc/config2.properties
coordinator=false
node-scheduler.include-coordinator=true
http-server.http.port=8002
query.max-memory=24GB
query.max-memory-per-node=8GB
discovery-server.enabled=true
discovery.uri=http://localhost:8001
EOF
cat << 'EOF' > etc/config3.properties
coordinator=true
node-scheduler.include-coordinator=true
http-server.http.port=8003
query.max-memory=24GB
query.max-memory-per-node=8GB
discovery-server.enabled=true
discovery.uri=http://localhost:8001
EOF
cat << 'EOF' > etc/node1.properties
node.environment=test
node.id=671d18f9-dd0f-412d-b18c-fe6d7989b040
node.data-dir=/usr/local/Cellar/presto/0.196/data/node1
EOF
cat << 'EOF' > etc/node2.properties
node.environment=test
node.id=e72fdd91-a135-4936-9a3e-f888c5106ed9
node.data-dir=/usr/local/Cellar/presto/0.196/data/node2
EOF
cat << 'EOF' > etc/node3.properties
node.environment=test
node.id=6ab76715-1812-4093-95cf-1945f4cfefe3
node.data-dir=/usr/local/Cellar/presto/0.196/data/node3
EOF
p.s. If you want to restrict operation, please add access-control.properties as below, only permit read operation.
cat << 'EOF' > etc/access-control.properties
access-control.name=read-only
EOF
3.start presto server
bin/launcher start --config=etc/config1.properties --node-config=etc/node1.properties
bin/launcher start --config=etc/config2.properties --node-config=etc/node2.properties
bin/launcher start --config=etc/config3.properties --node-config=etc/node3.properties
4.downlaod cli
wget https://repo1.maven.org/maven2/com/facebook/presto/presto-cli/0.196/presto-cli-0.196-executable.jar -O bin/presto-cli
chmod +x bin/presto-cli
5.create catalogs
cat << 'EOF' > etc/catalog/mysql.properties
connector.name=mysql
connection-url=jdbc:mysql://localhost:3306?useSSL=false
connection-user=presto
connection-password=presto
EOF
cat << 'EOF' > etc/catalog/hive.properties
connector.name=hive-hadoop2
hive.metastore.uri=thrift://localhost:9083
EOF
6.connect
bin/presto-cli --server localhost:8001 --catalog hive
presto> show catalogs;
Catalog
---------
hive
mysql
system
(3 rows)
Query 20180318_045410_00013_sq83e, FINISHED, 1 node
Splits: 1 total, 1 done (100.00%)
0:00 [0 rows, 0B] [0 rows/s, 0B/s]
Screenshot:
P.S. If build cluster, pay attention to below items:
1.node.id in node.properties in every node must be unique in the cluster, you could generate it by uuid/uuidgen.
2.query.max-memory-per-node in config.properties better to be half of -Xmx in jvm.config.
Recently I need do some statistic, so I choose ELK to build it. I will introduce about how to clean logs and send the logs to elasticsearch by logstash.
Logstash Version: 5.6.6
Firstly add a new configuration file named xxx.conf under config directory, the content are as below, please replace “xxx” with your business.
input {
file {
path => "/**/xxx.log"
codec => plain {
charset => "UTF-8"
}
tags => ["xxx"]
}
file {
path => "/**/xxx.log"
codec => plain {
charset => "UTF-8"
}
tags => ["xxx"]
}
}
filter {
if "xxx" in [tags] {
dissect {
mapping => {
"message" => "%{timestamp} - [%{thread}] - [%{level}] - [%{class}] - xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}"
}
}
}
if "xxx" in [tags] {
dissect {
mapping => {
"message" => "%{timestamp} - [%{thread}] - [%{level}] - [%{class}] - xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}, xxx:%{xxx}"
}
}
}
}
#replace @timestamp
#filter {
#date {
#match => ["timestamp", "yyyy-MM-dd HH:mm:ss,SSS"]
#target => ["@timestamp"]
#}
#}
output {
if "xxx" in [tags] {
elasticsearch {
index => "xxx"
hosts => ["http://xxx:9200"]
}
}
if "xxx" in [tags] {
elasticsearch {
index => "xxx"
hosts => ["http://xxx:9200"]
}
}
}
Then start logstash with this configuration file
bin/logstash -f config/xxx.conf
After this, please configure kibana dashborad, and you will get some cool charts.
P.S.
There are many kinds of input/filter/output, like jdbc/redis/kafka/mongodb, please refer to the official document.
If you are familiar with grok filter, you can also filter logs as below:
filter {
grok {
match => {
"message" => "%{TIMESTAMP_ISO8601:timestamp}%{SPACE}-%{SPACE}[.*]%{SPACE}-%{SPACE}xxx:%{GREEDYDATA:xxx},%{SPACE}xxx:%{GREEDYDATA:xxx},%{SPACE}xxx:%{GREEDYDATA:xxx},%{SPACE}xxx:%{GREEDYDATA:xxx},%{SPACE}xxx:%{GREEDYDATA:xxx},%{SPACE}xxx:%{GREEDYDATA:xxx},%{SPACE}xxx:%{GREEDYDATA:xxx},%{SPACE}xxx:%{GREEDYDATA:xxx}"
}
}
}
Recently I tested sqoop2 which has many new features compared to sqoop1, about the comparision, I think you could check here and stackoverflow, I will introduce about the operation manual.
1.install
wget http://ftp.jaist.ac.jp/pub/apache/sqoop/1.99.7/sqoop-1.99.7-bin-hadoop200.tar.gz
tar -vzxf sqoop-1.99.7-bin-hadoop200.tar.gz && cd sqoop-1.99.7-bin-hadoop200
2.replace @LOGDIR@/@BASEDIR@ in sqoop.properties
3.download mysql driver into server/lib
4.configure proxy user in core-site.xml
<property>
<name>hadoop.proxyuser.sqoop2.hosts</name>
<value>*</value>
</property>
<property>
<name>hadoop.proxyuser.sqoop2.groups</name>
<value>*</value>
</property>
5.verify & start sqoop2 server
bin/sqoop2-tool verify
bin/sqoop2-server start
6.start client & test
bin/sqoop2-shell
7.show the connectors
sqoop:000> show connector
+------------------------+---------+------------------------------------------------------------+----------------------+
| Name | Version | Class | Supported Directions |
+------------------------+---------+------------------------------------------------------------+----------------------+
| generic-jdbc-connector | 1.99.7 | org.apache.sqoop.connector.jdbc.GenericJdbcConnector | FROM/TO |
| kite-connector | 1.99.7 | org.apache.sqoop.connector.kite.KiteConnector | FROM/TO |
| oracle-jdbc-connector | 1.99.7 | org.apache.sqoop.connector.jdbc.oracle.OracleJdbcConnector | FROM/TO |
| ftp-connector | 1.99.7 | org.apache.sqoop.connector.ftp.FtpConnector | TO |
| hdfs-connector | 1.99.7 | org.apache.sqoop.connector.hdfs.HdfsConnector | FROM/TO |
| kafka-connector | 1.99.7 | org.apache.sqoop.connector.kafka.KafkaConnector | TO |
| sftp-connector | 1.99.7 | org.apache.sqoop.connector.sftp.SftpConnector | TO |
+------------------------+---------+------------------------------------------------------------+----------------------+
8.create links & show links
sqoop:000> create link -connector generic-jdbc-connector
sqoop:000> create link -connector hdfs-connector
sqoop:000> show link
+-------------+------------------------+---------+
| Name | Connector Name | Enabled |
+-------------+------------------------+---------+
| mysql-local | generic-jdbc-connector | true |
| hdfs-local | hdfs-connector | true |
+-------------+------------------------+---------+
sqoop:000> show link --all
2 link(s) to show:
link with name mysql-local (Enabled: true, Created by hongmeng.wang at 3/1/18 10:56 AM, Updated by hongmeng.wang at 3/1/18 12:51 PM)
Using Connector generic-jdbc-connector with name {1}
Database connection
Driver class: com.mysql.jdbc.Driver
Connection String: jdbc:mysql://localhost:3306
Username: root
Password:
Fetch Size: 100
Connection Properties:
protocol = tcp
useUnicode = true
characterEncoding = utf-8
autoReconnect = true
SQL Dialect
Identifier enclose: (blank, if use default, will get error)
link with name hdfs-local (Enabled: true, Created by hongmeng.wang at 3/1/18 10:58 AM, Updated by hongmeng.wang at 3/1/18 12:54 PM)
Using Connector hdfs-connector with name {1}
HDFS cluster
URI: hdfs://localhost:9000
Conf directory: /usr/local/Cellar/hadoop/2.8.2/libexec/etc/hadoop
Additional configs::
9.create job & show job
sqoop:000> create job -f "mysql-local" -t "hdfs-local"
sqoop:000> show job
+----+----------------------+--------------------------------------+-----------------------------+---------+
| Id | Name | From Connector | To Connector | Enabled |
+----+----------------------+--------------------------------------+-----------------------------+---------+
| 1 | mysql-2-hdfs-t1 | mysql-local (generic-jdbc-connector) | hdfs-local (hdfs-connector) | true |
+----+----------------------+--------------------------------------+-----------------------------+---------+
sqoop:000> show job --all
1 job(s) to show:
Job with name mysql-2-hdfs-segment (Enabled: true, Created by hongmeng.wang at 3/1/18 11:06 AM, Updated by hongmeng.wang at 3/1/18 11:39 AM)
Throttling resources
Extractors:
Loaders:
Classpath configuration
Extra mapper jars:
From link: mysql-local
Database source
Schema name: test
Table name: t1
SQL statement:
Column names:
Partition column: id
Partition column nullable:
Boundary query:
Incremental read
Check column:
Last value:
To link: hdfs-local
Target configuration
Override null value: true
Null value:
File format: TEXT_FILE
Compression codec: NONE
Custom codec:
Output directory: /sqoop/mysql/test
Append mode:
10.start job & check job’s status
sqoop:000> start job -name mysql-2-hdfs-segment
Submission details
Job Name: mysql-2-hdfs-segment
Server URL: http://localhost:12000/sqoop/
Created by: sqoop2
Creation date: 2018-03-01 13:53:37 JST
Lastly updated by: sqoop2
External ID: job_1519869491258_0001
http://localhost:8088/proxy/application_1519869491258_0001/
2018-03-01 13:53:37 JST: BOOTING - Progress is not available
sqoop:000> status job -n mysql-2-hdfs-segment
Submission details
Job Name: mysql-2-hdfs-segment
Server URL: http://localhost:12000/sqoop/
Created by: sqoop2
Creation date: 2018-03-01 14:01:54 JST
Lastly updated by: sqoop2
External ID: job_1519869491258_0002
http://localhost:8088/proxy/application_1519869491258_0002/
2018-03-01 14:03:31 JST: BOOTING - 0.00 %
Issues
1.modify “org.apache.sqoop.submission.engine.mapreduce.configuration.directory=”directory of hadoop configuration” in conf/sqoop.properties if you got below error when executing bin/sqoop2-tool verify
Exception in thread "main" java.lang.RuntimeException: Failure in server initialization
at org.apache.sqoop.core.SqoopServer.initialize(SqoopServer.java:68)
at org.apache.sqoop.server.SqoopJettyServer.<init>(SqoopJettyServer.java:67)
at org.apache.sqoop.server.SqoopJettyServer.main(SqoopJettyServer.java:177)
Caused by: org.apache.sqoop.common.SqoopException: MAPREDUCE_0002:Failure on submission engine initialization - Invalid Hadoop configuration directory (not a directory or permission issues): /etc/hadoop/conf/
at org.apache.sqoop.submission.mapreduce.MapreduceSubmissionEngine.initialize(MapreduceSubmissionEngine.java:97)
at org.apache.sqoop.driver.JobManager.initialize(JobManager.java:257)
at org.apache.sqoop.core.SqoopServer.initialize(SqoopServer.java:64)
... 2 more
2.check $CLASSPATH and $HADOOP_CLASSPATH, maybe some jars conflict if got below error:
Caused by: java.lang.SecurityException: sealing violation: package org.apache.derby.impl.services.locks is sealed
at java.net.URLClassLoader.getAndVerifyPackage(URLClassLoader.java:399)
at java.net.URLClassLoader.definePackageInternal(URLClassLoader.java:419)
at java.net.URLClassLoader.defineClass(URLClassLoader.java:451)
at java.net.URLClassLoader.access$100(URLClassLoader.java:73)
at java.net.URLClassLoader$1.run(URLClassLoader.java:368)
at java.net.URLClassLoader$1.run(URLClassLoader.java:362)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:361)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:335)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at java.lang.ClassLoader.defineClass1(Native Method)
at java.lang.ClassLoader.defineClass(ClassLoader.java:763)
at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
at java.net.URLClassLoader.defineClass(URLClassLoader.java:467)
at java.net.URLClassLoader.access$100(URLClassLoader.java:73)
at java.net.URLClassLoader$1.run(URLClassLoader.java:368)
at java.net.URLClassLoader$1.run(URLClassLoader.java:362)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:361)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:335)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:264)
at org.apache.derby.impl.services.monitor.BaseMonitor.getImplementations(Unknown Source)
at org.apache.derby.impl.services.monitor.BaseMonitor.getDefaultImplementations(Unknown Source)
at org.apache.derby.impl.services.monitor.BaseMonitor.runWithState(Unknown Source)
at org.apache.derby.iampl.services.monitor.FileMonitor.<init>(Unknown Source)
at org.apache.derby.iapi.services.monitor.Monitor.startMonitor(Unknown Source)
at org.apache.derby.iapi.jdbc.JDBCBoot.boot(Unknown Source)
at org.apache.derby.jdbc.EmbeddedDriver.boot(Unknown Source)
at org.apache.derby.jdbc.EmbeddedDriver.<clinit>(Unknown Source)
... 11 more
Modify capacity-scheduler.xml under $HADOOP_CONF_DIR, and I configured 3 queues: default, business, platform
<configuration>
<property>
<name>yarn.scheduler.capacity.maximum-applications</name>
<value>10000</value>
</property>
<property>
<name>yarn.scheduler.capacity.maximum-am-resource-percent</name>
<value>0.1</value>
</property>
<property>
<name>yarn.scheduler.capacity.resource-calculator</name>
<value>org.apache.hadoop.yarn.util.resource.DefaultResourceCalculator</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.queues</name>
<value>default,business,platform</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.default.capacity</name>
<value>50</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.default.user-limit-factor</name>
<value>1</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.default.maximum-capacity</name>
<value>50</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.default.state</name>
<value>RUNNING</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.default.acl_submit_applications</name>
<value>*</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.default.acl_administer_queue</name>
<value>*</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.business.capacity</name>
<value>30</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.business.user-limit-factor</name>
<value>1</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.business.maximum-capacity</name>
<value>30</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.business.state</name>
<value>RUNNING</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.business.acl_submit_applications</name>
<value>*</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.business.acl_administer_queue</name>
<value>*</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.platform.capacity</name>
<value>20</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.platform.user-limit-factor</name>
<value>1</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.platform.maximum-capacity</name>
<value>20</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.platform.state</name>
<value>RUNNING</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.platform.acl_submit_applications</name>
<value>*</value>
</property>
<property>
<name>yarn.scheduler.capacity.root.platform.acl_administer_queue</name>
<value>*</value>
</property>
<property>
<name>yarn.scheduler.capacity.node-locality-delay</name>
<value>40</value>
</property>
<property>
<name>yarn.scheduler.capacity.queue-mappings</name>
<value></value>
</property>
<property>
<name>yarn.scheduler.capacity.queue-mappings-override.enable</name>
<value>false</value>
</property>
<property>
<name>yarn.scheduler.capacity.per-node-heartbeat.maximum-offswitch-assignments</name>
<value>1</value>
</property>
</configuration>
install sqoop1 on mac
brew install sqoop
#if you have set env profiles, uncomment profiles in conf/sqoop-env.sh
1.MySQL -> HDFS
1.1.import table
sqoop import --connect jdbc:mysql://localhost/test --direct --username root --P --table t1 --warehouse-dir /mysql/test --fields-terminated-by ','
1.2.import schema
sqoop import-all-tables --connect jdbc:mysql://localhost/test --direct --username root -P --warehouse-dir /mysql/test --fields-terminated-by ','
2.MySQL -> Hive
2.1.import definition
sqoop create-hive-table --connect jdbc:mysql://localhost/test --table t1 --username root --P --hive-database test
2.2.import table
sqoop import --connect jdbc:mysql://localhost/test --username root --P --table t1 --hive-import --hive-database test --hive-table t1 --fields-terminated-by ','
2.3.import schema
sqoop import-all-tables --connect jdbc:mysql://localhost/test --username root --P --hive-import --hive-database test --fields-terminated-by ','
3.MySQL -> HBase
3.1.definition
sqoop import --connect jdbc:mysql://localhost/test --username root --P --table t1
3.2.import table, need create table in hbase first
sqoop import --connect jdbc:mysql://localhost/test --username root --P --table t1 --hbase-bulkload --hbase-table test.t1 --column-family basic --fields-terminated-by ','
3.3.import table without creating table in hbase, but pay attention to hbase/sqoop version
sqoop import --connect jdbc:mysql://localhost/test --username root --P --table t1 --hbase-bulkload --hbase-create-table --hbase-table test.t1 --column-family basic --fields-terminated-by ','
4.HDFS/Hive/HBase -> MySQL
sqoop export --connect jdbc:mysql://localhost/test --username root --P --table t1 --export-dir /user/hive/warehouse/test.db/t1 --fields-terminated-by ','
When I load data from hdfs to hbase, I got error:
Caused by: java.lang.IllegalArgumentException: No columns to insert
at org.apache.hadoop.hbase.client.HTable.validatePut(HTable.java:1505)
at org.apache.hadoop.hbase.client.BufferedMutatorImpl.validatePut(BufferedMutatorImpl.java:147)
at org.apache.hadoop.hbase.client.BufferedMutatorImpl.doMutate(BufferedMutatorImpl.java:134)
at org.apache.hadoop.hbase.client.BufferedMutatorImpl.mutate(BufferedMutatorImpl.java:98)
at org.apache.hadoop.hbase.client.HTable.put(HTable.java:1028)
at org.apache.hadoop.hive.hbase.HiveHBaseTableOutputFormat$MyRecordWriter.write(HiveHBaseTableOutputFormat.java:146)
at org.apache.hadoop.hive.hbase.HiveHBaseTableOutputFormat$MyRecordWriter.write(HiveHBaseTableOutputFormat.java:117)
at org.apache.hadoop.hive.ql.io.HivePassThroughRecordWriter.write(HivePassThroughRecordWriter.java:40)
at org.apache.hadoop.hive.ql.exec.FileSinkOperator.process(FileSinkOperator.java:762)
at org.apache.hadoop.hive.ql.exec.Operator.forward(Operator.java:897)
at org.apache.hadoop.hive.ql.exec.SelectOperator.process(SelectOperator.java:95)
at org.apache.hadoop.hive.ql.exec.Operator.forward(Operator.java:897)
at org.apache.hadoop.hive.ql.exec.TableScanOperator.process(TableScanOperator.java:130)
at org.apache.hadoop.hive.ql.exec.MapOperator$MapOpCtx.forward(MapOperator.java:148)
at org.apache.hadoop.hive.ql.exec.MapOperator.process(MapOperator.java:547)
... 9 more
After reading the document, it said that hbase doesn’t support null value, I checked hdfs files, it indeed contained null value in some properties.
So I modified the data and reloaded to hbase, I didn’t get the error any more.
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I test the performance of MR vs Tez again on cluster, I created a new table which contains 28,872,974 rows, below are cluster servers:
|
Host |
OS |
Memory |
CPU |
Disk |
Region |
| master.c.ambari-195807.internal |
CentOS 7 |
13 GB |
Intel Ivy Bridge: 2 |
200G |
asia-east1-a |
| slave1.c.ambari-195807.internal |
CentOS 7 |
13 GB |
Intel Ivy Bridge: 2 |
200G |
asia-east1-a |
| slave2.c.ambari-195807.internal |
CentOS 7 |
13 GB |
Intel Ivy Bridge: 2 |
200G |
asia-east1-a |
| slave3.c.ambari-195807.internal |
CentOS 7 |
13 GB |
Intel Ivy Bridge: 2 |
200G |
asia-east1-a |
1.MR
1.1.create table
hive> CREATE TABLE gbif.gbif_0004998
> STORED AS ORC
> TBLPROPERTIES("orc.compress"="snappy")
> AS SELECT * FROM gbif.gbif_0004998_ori;
WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.
Query ID = gizmo_20180225064259_8df29800-b260-48f5-a409-80d6ea5200ad
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1519536795015_0001, Tracking URL = http://master.c.ambari-195807.internal:8088/proxy/application_1519536795015_0001/
Kill Command = /opt/apps/hadoop-2.8.3/bin/hadoop job -kill job_1519536795015_0001
Hadoop job information for Stage-1: number of mappers: 43; number of reducers: 0
2018-02-25 06:43:15,110 Stage-1 map = 0%, reduce = 0%
2018-02-25 06:44:15,419 Stage-1 map = 0%, reduce = 0%, Cumulative CPU 231.6 sec
2018-02-25 06:44:36,386 Stage-1 map = 2%, reduce = 0%, Cumulative CPU 380.45 sec
2018-02-25 06:44:37,810 Stage-1 map = 3%, reduce = 0%, Cumulative CPU 386.09 sec
2018-02-25 06:44:41,695 Stage-1 map = 5%, reduce = 0%, Cumulative CPU 422.02 sec
...
...
2018-02-25 06:47:36,112 Stage-1 map = 97%, reduce = 0%, Cumulative CPU 1388.9 sec
2018-02-25 06:47:38,185 Stage-1 map = 98%, reduce = 0%, Cumulative CPU 1392.1 sec
2018-02-25 06:47:45,434 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1402.14 sec
MapReduce Total cumulative CPU time: 23 minutes 22 seconds 140 msec
Ended Job = job_1519536795015_0001
Stage-4 is selected by condition resolver.
Stage-3 is filtered out by condition resolver.
Stage-5 is filtered out by condition resolver.
Moving data to directory hdfs://master.c.ambari-195807.internal:9000/user/hive/warehouse/gbif.db/.hive-staging_hive_2018-02-25_06-42-59_672_2925216554228494176-1/-ext-10002
Moving data to directory hdfs://master.c.ambari-195807.internal:9000/user/hive/warehouse/gbif.db/gbif_0004998
MapReduce Jobs Launched:
Stage-Stage-1: Map: 43 Cumulative CPU: 1402.14 sec HDFS Read: 11519083564 HDFS Write: 1210708016 SUCCESS
Total MapReduce CPU Time Spent: 23 minutes 22 seconds 140 msec
OK
Time taken: 288.681 seconds
1.2.query by on condition
hive> select count(*) as total from gbif_0004998 where mediatype = 'STILLIMAGE';
WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.
Query ID = gizmo_20180225065438_d2343424-5178-4c44-8b9d-0b28f8b701fa
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1519536795015_0002, Tracking URL = http://master.c.ambari-195807.internal:8088/proxy/application_1519536795015_0002/
Kill Command = /opt/apps/hadoop-2.8.3/bin/hadoop job -kill job_1519536795015_0002
Hadoop job information for Stage-1: number of mappers: 5; number of reducers: 1
2018-02-25 06:54:50,078 Stage-1 map = 0%, reduce = 0%
2018-02-25 06:55:02,485 Stage-1 map = 40%, reduce = 0%, Cumulative CPU 21.01 sec
2018-02-25 06:55:03,544 Stage-1 map = 80%, reduce = 0%, Cumulative CPU 38.51 sec
2018-02-25 06:55:06,704 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 49.23 sec
2018-02-25 06:55:09,881 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 51.88 sec
MapReduce Total cumulative CPU time: 51 seconds 880 msec
Ended Job = job_1519536795015_0002
MapReduce Jobs Launched:
Stage-Stage-1: Map: 5 Reduce: 1 Cumulative CPU: 51.88 sec HDFS Read: 1936305 HDFS Write: 107 SUCCESS
Total MapReduce CPU Time Spent: 51 seconds 880 msec
OK
2547716
Time taken: 32.292 seconds, Fetched: 1 row(s)
1.3.query by two conditions
hive> select count(*) as total from gbif_0004998 where mediatype = 'STILLIMAGE' and year > 1900;
WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.
Query ID = gizmo_20180225081238_766d3707-7eb4-4818-860e-887c48d507ce
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1519545228015_0002, Tracking URL = http://master.c.ambari-195807.internal:8088/proxy/application_1519545228015_0002/
Kill Command = /opt/apps/hadoop-2.8.3/bin/hadoop job -kill job_1519545228015_0002
Hadoop job information for Stage-1: number of mappers: 5; number of reducers: 1
2018-02-25 08:17:31,666 Stage-1 map = 0%, reduce = 0%
2018-02-25 08:17:43,866 Stage-1 map = 20%, reduce = 0%, Cumulative CPU 10.58 sec
2018-02-25 08:17:46,045 Stage-1 map = 60%, reduce = 0%, Cumulative CPU 34.12 sec
2018-02-25 08:17:54,996 Stage-1 map = 80%, reduce = 0%, Cumulative CPU 41.73 sec
2018-02-25 08:17:57,126 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 51.37 sec
2018-02-25 08:17:58,192 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 53.72 sec
MapReduce Total cumulative CPU time: 53 seconds 720 msec
Ended Job = job_1519545228015_0002
MapReduce Jobs Launched:
Stage-Stage-1: Map: 5 Reduce: 1 Cumulative CPU: 53.72 sec HDFS Read: 8334197 HDFS Write: 107 SUCCESS
Total MapReduce CPU Time Spent: 53 seconds 720 msec
OK
2547716
Time taken: 321.138 seconds, Fetched: 1 row(s)
2.Tez
2.1.create table
hive> CREATE TABLE gbif.gbif_0004998
> STORED AS ORC
> TBLPROPERTIES("orc.compress"="snappy")
> AS SELECT * FROM gbif.gbif_0004998_ori;
Query ID = gizmo_20180225075657_bae527a7-7cbd-46d9-afbf-70a5adcdee7c
Total jobs = 1
Launching Job 1 out of 1
Status: Running (Executing on YARN cluster with App id application_1519545228015_0001)
----------------------------------------------------------------------------------------------
VERTICES MODE STATUS TOTAL COMPLETED RUNNING PENDING FAILED KILLED
----------------------------------------------------------------------------------------------
Map 1 .......... container SUCCEEDED 1 1 0 0 0 0
----------------------------------------------------------------------------------------------
VERTICES: 01/01 [==========================>>] 100% ELAPSED TIME: 639.61 s
----------------------------------------------------------------------------------------------
Moving data to directory hdfs://master.c.ambari-195807.internal:9000/user/hive/warehouse/gbif.db/gbif_0004998
OK
Time taken: 664.817 seconds
2.2.query by one condition
hive> select count(*) as total from gbif_0004998 where mediatype = 'STILLIMAGE';
Query ID = gizmo_20180225080856_d1f13489-30b0-4045-bdeb-e3e5e085e736
Total jobs = 1
Launching Job 1 out of 1
Status: Running (Executing on YARN cluster with App id application_1519545228015_0001)
----------------------------------------------------------------------------------------------
VERTICES MODE STATUS TOTAL COMPLETED RUNNING PENDING FAILED KILLED
----------------------------------------------------------------------------------------------
Map 1 .......... container SUCCEEDED 5 5 0 0 0 0
Reducer 2 ...... container SUCCEEDED 1 1 0 0 0 0
----------------------------------------------------------------------------------------------
VERTICES: 02/02 [==========================>>] 100% ELAPSED TIME: 17.91 s
----------------------------------------------------------------------------------------------
OK
2547716
Time taken: 19.255 seconds, Fetched: 1 row(s)
2.2.query by two conditions
hive> select count(*) as total from gbif_0004998 where mediatype = 'STILLIMAGE' and year > 1900;
Query ID = gizmo_20180225081200_0279f8e6-544b-4573-858b-33f48bf1fa35
Total jobs = 1
Launching Job 1 out of 1
Status: Running (Executing on YARN cluster with App id application_1519545228015_0001)
----------------------------------------------------------------------------------------------
VERTICES MODE STATUS TOTAL COMPLETED RUNNING PENDING FAILED KILLED
----------------------------------------------------------------------------------------------
Map 1 .......... container SUCCEEDED 5 5 0 0 0 0
Reducer 2 ...... container SUCCEEDED 1 1 0 0 0 0
----------------------------------------------------------------------------------------------
VERTICES: 02/02 [==========================>>] 100% ELAPSED TIME: 16.96 s
----------------------------------------------------------------------------------------------
OK
2547716
Time taken: 17.635 seconds, Fetched: 1 row(s)
3.Summary
Rows: 28,872,974
| Type | Create Table | Query By One Condition | Query By Two Conditions |
| MR | 288.681s | 32.292s | 321.138s |
| Tez | 664.817s | 19.255s | 17.635s |
According to the result, MR is quicker than Tez on creation, but slower than Tez on query, along with query condition’s increase, MR’s query performance became worse.
But why MR is quicker than Tez on creation, currently I don’t know, need to be investigated later.
Maybe it has relationship with storage, I have checked the filesystem after the two kinds of creation, it’s different. MR has many small files, but Tez has one much bigger file.
MR generated files
Tez generated files
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