If you are a new guy for Apache Griffin, please follow the instructions below to deploy Apache Griffin in your environment. Note that those steps will install all products in one physical machine, so you have to tune configurations depending on true topology.
Firstly you need to install and configure following software products, here we use ubuntu-18.10 as sample OS to prepare all dependencies.
# put all download packages into /apache folder $ mkdir /home/<user>/software $ mkdir /home/<user>/software/data $ sudo ln -s /home/<user>/software /apache $ sudo ln -s /apache/data /data $ mkdir /apache/tmp $ mkdir /apache/tmp/hive
$ sudo apt install openjdk-8-jre-headless $ java -version openjdk version "1.8.0_191" OpenJDK Runtime Environment (build 1.8.0_191-8u191-b12-0ubuntu0.18.10.1-b12) OpenJDK 64-Bit Server VM (build 25.191-b12, mixed mode)
# PostgreSQL $ sudo apt install postgresql-10 # MySQL $ sudo apt install mysql-server-5.7
$ sudo apt install nodejs $ sudo apt install npm $ node -v $ npm -v
Spark (version 2.2.1), if you want to install Pseudo Distributed/Single Node Cluster, you can get some helps here.
ElasticSearch (5.0 or later versions). ElasticSearch works as a metrics collector, Apache Griffin produces metrics into it, and our default UI gets metrics from it, you can use them by your own way as well.
Create database ‘quartz’ in PostgreSQL
createdb -O <username> quartz
Init quartz tables in PostgreSQL using Init_quartz_postgres.sql
psql -p <port> -h <host address> -U <username> -f Init_quartz_postgres.sql quartz
Create database ‘quartz’ in MySQL
mysql -u <username> -e "create database quartz" -p
Init quartz tables in MySQL using Init_quartz_mysql_innodb.sql
mysql -u <username> -p quartz < Init_quartz_mysql_innodb.sql
Export those variables below, or create griffin_env.sh and put it into .bashrc.
#!/bin/bash export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64 export HADOOP_HOME=/apache/hadoop export HADOOP_COMMON_HOME=/apache/hadoop export HADOOP_COMMON_LIB_NATIVE_DIR=/apache/hadoop/lib/native export HADOOP_HDFS_HOME=/apache/hadoop export HADOOP_INSTALL=/apache/hadoop export HADOOP_MAPRED_HOME=/apache/hadoop export HADOOP_USER_CLASSPATH_FIRST=true export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop export SPARK_HOME=/apache/spark export LIVY_HOME=/apache/livy export HIVE_HOME=/apache/hive export YARN_HOME=/apache/hadoop export SCALA_HOME=/apache/scala export PATH=$PATH:$HIVE_HOME/bin:$HADOOP_HOME/bin:$SPARK_HOME/bin:$LIVY_HOME/bin:$SCALA_HOME/bin
Put site-specific property overrides in this file /apache/hadoop/etc/hadoop/core-site.xml
<configuration> <property> <name>fs.defaultFS</name> <value>hdfs://127.0.0.1:9000</value> </property> </configuration>
Put site-specific property overrides in this file /apache/hadoop/etc/hadoop/hdfs-site.xml
<configuration> <property> <name>dfs.namenode.logging.level</name> <value>warn</value> </property> <property> <name>dfs.replication</name> <value>1</value> </property> <property> <name>dfs.namenode.name.dir</name> <value>file:///data/hadoop-data/nn</value> </property> <property> <name>dfs.datanode.data.dir</name> <value>file:///data/hadoop-data/dn</value> </property> <property> <name>dfs.namenode.checkpoint.dir</name> <value>file:///data/hadoop-data/snn</value> </property> <property> <name>dfs.webhdfs.enabled</name> <value>true</value> </property> <property> <name>dfs.datanode.use.datanode.hostname</name> <value>false</value> </property> <property> <name>dfs.namenode.datanode.registration.ip-hostname-check</name> <value>false</value> </property> </configuration>
# format name node # NOTE: if you already have executed namenode-format before, it'll change cluster ID in # name node's VERSION file after you run it again. so you need to guarantee same cluster ID # in data node's VERSION file, otherwise data node will fail to start up. # VERSION file resides in /apache/data/hadoop-data/nn, snn, dn denoted in previous config. /apache/hadoop/bin/hdfs namenode -format # start namenode/secondarynamenode/datanode # NOTE: you should use 'ps -ef|grep java' to check if namenode/secondary namenode/datanode # are available after starting dfs service. # if there is any error, please find clues from /apache/hadoop/logs/ /apache/hadoop/sbin/start-dfs.sh # stop all nodes /apache/hadoop/sbin/stop-dfs.sh
Here you can access http://127.0.0.1:50070/ to check name node.
# manually clear the ResourceManager state store /apache/hadoop/bin/yarn resourcemanager -format-state-store # startup the ResourceManager /apache/hadoop/sbin/yarn-daemon.sh start resourcemanager # stop the ResourceManager /apache/hadoop/sbin/yarn-daemon.sh stop resourcemanager
Here you can access http://127.0.0.1:8088/cluster to check hadoop cluster.
Hadoop daemons also expose some information over HTTP like http://127.0.0.1:8088/stacks. Please refer to blog
# startup the NodeManager /apache/hadoop/sbin/yarn-daemon.sh start nodemanager # stop the NodeManager /apache/hadoop/sbin/yarn-daemon.sh stop nodemanager
Here you can access http://127.0.0.1:8088/cluster/nodes to check hadoop nodes, you should see one node in the list.
# startup the HistoryServer /apache/hadoop/sbin/mr-jobhistory-daemon.sh start historyserver # stop the HistoryServer /apache/hadoop/sbin/mr-jobhistory-daemon.sh stop historyserver
+++ hive/conf/hive-site.xml 2018-12-16 11:17:51.000000000 +0800 @@ -72,12 +72,12 @@ </property> <property> <name>hive.exec.local.scratchdir</name> - <value>${system:java.io.tmpdir}/${system:user.name}</value> + <value>/apache/tmp/hive</value> <description>Local scratch space for Hive jobs</description> </property> <property> <name>hive.downloaded.resources.dir</name> - <value>${system:java.io.tmpdir}/${hive.session.id}_resources</value> + <value>/apache/tmp/hive/${hive.session.id}_resources</value> <description>Temporary local directory for added resources in the remote file system.</description> </property> <property> @@ -368,7 +368,7 @@ </property> <property> <name>hive.metastore.uris</name> - <value/> + <value>thrift://127.0.0.1:9083</value> <description>Thrift URI for the remote metastore.</description> </property> <property> @@ -527,7 +527,7 @@ </property> <property> <name>javax.jdo.option.ConnectionPassword</name> - <value>mine</value> + <value>secret</value> <description>password to use against metastore database</description> </property> <property> @@ -542,7 +542,7 @@ </property> <property> <name>javax.jdo.option.ConnectionURL</name> - <value>jdbc:derby:;databaseName=metastore_db;create=true</value> + <value>jdbc:postgresql://127.0.0.1/myDB?ssl=false</value> <description> JDBC connect string for a JDBC metastore. To use SSL to encrypt/authenticate the connection, provide database-specific SSL flag in the connection URL. @@ -1017,7 +1017,7 @@ </property> <property> <name>javax.jdo.option.ConnectionDriverName</name> - <value>org.apache.derby.jdbc.EmbeddedDriver</value> + <value>org.postgresql.Driver</value> <description>Driver class name for a JDBC metastore</description> </property> <property> @@ -1042,7 +1042,7 @@ </property> <property> <name>javax.jdo.option.ConnectionUserName</name> - <value>APP</value> + <value>king</value> <description>Username to use against metastore database</description> </property> <property> @@ -1682,7 +1682,7 @@ </property> <property> <name>hive.querylog.location</name> - <value>${system:java.io.tmpdir}/${system:user.name}</value> + <value>/apache/tmp/hive</value> <description>Location of Hive run time structured log file</description> </property> <property> @@ -3973,7 +3973,7 @@ </property> <property> <name>hive.server2.logging.operation.log.location</name> - <value>${system:java.io.tmpdir}/${system:user.name}/operation_logs</value> + <value>/apache/tmp/hive/operation_logs</value> </property> <property>
# start hive metastore service /apache/hive/bin/hive --service metastore
Check $SPARK_HOME/conf/spark-default.conf
spark.master yarn-cluster spark.serializer org.apache.spark.serializer.KryoSerializer spark.yarn.jars hdfs:///home/spark_lib/* spark.yarn.dist.files hdfs:///home/spark_conf/hive-site.xml spark.sql.broadcastTimeout 500
Check $SPARK_HOME/conf/spark-env.sh
HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop SPARK_MASTER_HOST=localhost SPARK_MASTER_PORT=7077 SPARK_MASTER_WEBUI_PORT=8082 SPARK_LOCAL_IP=localhost SPARK_PID_DIR=/apache/pids
Upload some files otherwise you will hit Error: Could not find or load main class org.apache.spark.deploy.yarn.ApplicationMaster, when you schedule spark applications.
hdfs dfs -mkdir /home/spark_lib hdfs dfs -mkdir /home/spark_conf hdfs dfs -put $SPARK_HOME/jars/* hdfs:///home/spark_lib/ hdfs dfs -put $HIVE_HOME/conf/hive-site.xml hdfs:///home/spark_conf/
cp /apache/hive/conf/hive-site.xml /apache/spark/conf/ # start master and slave nodes /apache/spark/sbin/start-master.sh /apache/spark/sbin/start-slave.sh spark://localhost:7077 # stop master and slave nodes /apache/spark/sbin/stop-slaves.sh /apache/spark/sbin/stop-master.sh # stop all /apache/spark/sbin/stop-all.sh
Apache Griffin need to schedule spark jobs by server, we use livy to submit our jobs.
mkdir /apache/livy/logs
Update $LIVY_HOME/conf/livy.conf
# update /apache/livy/conf/livy.conf livy.server.host = 127.0.0.1 livy.spark.master = yarn livy.spark.deployMode = cluster livy.repl.enableHiveContext = true livy.server.port 8998
/apache/livy/bin/livy-server start
Update $ES_HOME/config/elasticsearch.yml
network.host: 127.0.0.1 http.cors.enabled: true http.cors.allow-origin: "*"
/apache/elastic/bin/elasticsearch
You can access http://127.0.0.1:9200/ to check elasticsearch service.
You can download latest package from official link, or locally build on source codes.
Before building Griffin, you have to update those configuration depending on previous steps's configuration.
You can get more detailed configuration description in here.
# Apache Griffin server port (default 8080)
server.port = 8080
spring.application.name=griffin_service
# db configuration
spring.datasource.url=jdbc:postgresql://localhost:5432/myDB?autoReconnect=true&useSSL=false
spring.datasource.username=king
spring.datasource.password=secret
spring.jpa.generate-ddl=true
spring.datasource.driver-class-name=org.postgresql.Driver
spring.jpa.show-sql=true
# Hive metastore
hive.metastore.uris=thrift://localhost:9083
hive.metastore.dbname=default
hive.hmshandler.retry.attempts=15
hive.hmshandler.retry.interval=2000ms
# Hive cache time
cache.evict.hive.fixedRate.in.milliseconds=900000
# Kafka schema registry
kafka.schema.registry.url=http://localhost:8081
# Update job instance state at regular intervals
jobInstance.fixedDelay.in.milliseconds=60000
# Expired time of job instance which is 7 days that is 604800000 milliseconds.Time unit only supports milliseconds
jobInstance.expired.milliseconds=604800000
# schedule predicate job every 5 minutes and repeat 12 times at most
#interval time unit s:second m:minute h:hour d:day,only support these four units
predicate.job.interval=5m
predicate.job.repeat.count=12
# external properties directory location
external.config.location=
# external BATCH or STREAMING env
external.env.location=
# login strategy ("default" or "ldap")
login.strategy=default
# ldap
ldap.url=ldap://hostname:port
ldap.email=@example.com
ldap.searchBase=DC=org,DC=example
ldap.searchPattern=(sAMAccountName={0})
# hdfs default name
fs.defaultFS=
# elasticsearch
# elasticsearch.host = <IP>
# elasticsearch.port = <elasticsearch rest port>
# elasticsearch.user = user
# elasticsearch.password = password
elasticsearch.host=localhost
elasticsearch.port=9200
elasticsearch.scheme=http
# livy
livy.uri=http://localhost:8998/batches
# yarn url
yarn.uri=http://localhost:8088
# griffin event listener
internal.event.listeners=GriffinJobEventHook
org.quartz.scheduler.instanceName=spring-boot-quartz org.quartz.scheduler.instanceId=AUTO org.quartz.threadPool.threadCount=5 org.quartz.jobStore.class=org.quartz.impl.jdbcjobstore.JobStoreTX # If you use postgresql, set this property value to org.quartz.impl.jdbcjobstore.PostgreSQLDelegate # If you use mysql, set this property value to org.quartz.impl.jdbcjobstore.StdJDBCDelegate # If you use h2, it's ok to set this property value to StdJDBCDelegate, PostgreSQLDelegate or others org.quartz.jobStore.driverDelegateClass=org.quartz.impl.jdbcjobstore.PostgreSQLDelegate org.quartz.jobStore.useProperties=true org.quartz.jobStore.misfireThreshold=60000 org.quartz.jobStore.tablePrefix=QRTZ_ org.quartz.jobStore.isClustered=true org.quartz.jobStore.clusterCheckinInterval=20000
griffin measure path is the location where you should put the jar file of measure module.
{
"file": "hdfs:///<griffin measure path>/griffin-measure.jar",
"className": "org.apache.griffin.measure.Application",
"name": "griffin",
"queue": "default",
"numExecutors": 3,
"executorCores": 1,
"driverMemory": "1g",
"executorMemory": "1g",
"conf": {
"spark.yarn.dist.files": "hdfs:///<path to>/hive-site.xml"
},
"files": [
],
"jars": [
]
}
Adjust sinks according to your requirement. At least, you will need to adjust HDFS output directory (hdfs:///griffin/persist by default), and Elasticsearch URL (http://es:9200/griffin/accuracy by default). Similar changes are required in env_streaming.json.
{
"spark": {
"log.level": "WARN"
},
"sinks": [
{
"type": "CONSOLE",
"config": {
"max.log.lines": 10
}
},
{
"type": "HDFS",
"config": {
"path": "hdfs:///griffin/persist",
"max.persist.lines": 10000,
"max.lines.per.file": 10000
}
},
{
"type": "ELASTICSEARCH",
"config": {
"method": "post",
"api": "http://127.0.0.1:9200/griffin/accuracy",
"connection.timeout": "1m",
"retry": 10
}
}
],
"griffin.checkpoint": []
}
It's easy to build Griffin, just run maven command mvn clean install. Successfully building, you can get two jars service-0.4.0.jar,measure-0.4.0.jar from target folder in service and measure module.
Upload measure's jar to hadoop folder.
# change jar name mv measure-0.4.0.jar griffin-measure.jar mv service-0.4.0.jar griffin-service.jar # upload measure jar file hdfs dfs -put griffin-measure.jar /griffin/
Startup service.jar，run Griffin management service.
cd $GRIFFIN_HOME nohup java -jar griffin-service.jar>service.out 2>&1 &
After a few seconds, we can visit our default UI of Apache Griffin (by default the port of spring boot is 8080).
http://<your IP>:8080
You can conduct UI operations following the steps here.
Note: The UI does not support all the backend features, to experience the advanced features you can use service's api directly.
Griffin Service is regular Spring Boot application, so it supports all customizations from Spring Boot. To enable output compression, the following should be added to application.properties:
server.compression.enabled=true
server.compression.mime-types=application/json,application/xml,text/html,\
text/xml,text/plain,application/javascript,text/css
It is possible to enable SSL encryption for api and web endpoints. To do that, you will need to prepare keystore in Spring-compatible format (for example, PKCS12), and add the following values to application.properties:
server.ssl.key-store=/path/to/keystore.p12 server.ssl.key-store-password=yourpassword server.ssl.keyStoreType=PKCS12 server.ssl.keyAlias=your_key_alias
The following properties are available for LDAP:
{0} is replaced with user's login after ldap.email is concatenated. This expression is used to find user object in LDAP. Access is denied if filter expression did not match any users.$ hdfs dfs -ls / Found 3 items drwxr-xr-x - king supergroup 0 2019-02-21 17:25 /data drwx-wx-wx - king supergroup 0 2019-02-21 16:45 /tmp drwxr-xr-x - king supergroup 0 2019-02-26 08:48 /user $ hdfs dfs -mkdir /griffin $ hdfs dfs -ls / Found 4 items drwxr-xr-x - king supergroup 0 2019-02-21 17:25 /data drwxr-xr-x - king supergroup 0 2019-02-26 10:30 /griffin drwx-wx-wx - king supergroup 0 2019-02-21 16:45 /tmp drwxr-xr-x - king supergroup 0 2019-02-26 08:48 /user $ hdfs dfs -put griffin-measure.jar /griffin/ $ hdfs dfs -ls /griffin -rw-r--r-- 1 king supergroup 30927307 2019-02-26 10:36 /griffin/griffin-measure.jar
Here you can refer to dfs commands, get command examples.
# create /home/spark_conf # -p option behavior is much like Unix mkdir -p, creating parent directories along the path. hdfs dfs -mkdir -p /home/spark_conf # upload hive-site.xml hdfs dfs -put hive-site.xml /home/spark_conf/
# login hive client /apache/hive/bin/hive --database default # create demo tables hive> CREATE EXTERNAL TABLE `demo_src`( `id` bigint, `age` int, `desc` string) PARTITIONED BY ( `dt` string, `hour` string) ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' LOCATION 'hdfs://127.0.0.1:9000/griffin/data/batch/demo_src'; hive> CREATE EXTERNAL TABLE `demo_tgt`( `id` bigint, `age` int, `desc` string) PARTITIONED BY ( `dt` string, `hour` string) ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' LOCATION 'hdfs://127.0.0.1:9000/griffin/data/batch/demo_tgt'; # check tables created hive> show tables; OK demo_src demo_tgt Time taken: 0.04 seconds, Fetched: 2 row(s)
Check table definition.
hive> show create table demo_src; OK CREATE EXTERNAL TABLE `demo_src`( `id` bigint, `age` int, `desc` string) PARTITIONED BY ( `dt` string, `hour` string) ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' WITH SERDEPROPERTIES ( 'field.delim'='|', 'serialization.format'='|') STORED AS INPUTFORMAT 'org.apache.hadoop.mapred.TextInputFormat' OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat' LOCATION 'hdfs://127.0.0.1:9000/griffin/data/batch/demo_src' TBLPROPERTIES ( 'transient_lastDdlTime'='1551168613') Time taken: 3.762 seconds, Fetched: 20 row(s)
If the table definition is not correct, drop it.
hive> drop table if exists demo_src; OK Time taken: 3.764 seconds hive> drop table if exists demo_tgt; OK Time taken: 0.632 seconds
/apache/data/demo$ wget http://griffin.apache.org/data/batch/gen_demo_data.sh /apache/data/demo$ wget http://griffin.apache.org/data/batch/gen_delta_src.sh /apache/data/demo$ wget http://griffin.apache.org/data/batch/demo_basic /apache/data/demo$ wget http://griffin.apache.org/data/batch/delta_tgt /apache/data/demo$ wget http://griffin.apache.org/data/batch/insert-data.hql.template /apache/data/demo$ chmod 755 *.sh /apache/data/demo$ ./gen_demo_data.sh
Create gen-hive-data.sh
#!/bin/bash
#create table
hive -f create-table.hql
echo "create table done"
#current hour
sudo ./gen_demo_data.sh
cur_date=`date +%Y%m%d%H`
dt=${cur_date:0:8}
hour=${cur_date:8:2}
partition_date="dt='$dt',hour='$hour'"
sed s/PARTITION_DATE/$partition_date/ ./insert-data.hql.template > insert-data.hql
hive -f insert-data.hql
src_done_path=/griffin/data/batch/demo_src/dt=${dt}/hour=${hour}/_DONE
tgt_done_path=/griffin/data/batch/demo_tgt/dt=${dt}/hour=${hour}/_DONE
hadoop fs -mkdir -p /griffin/data/batch/demo_src/dt=${dt}/hour=${hour}
hadoop fs -mkdir -p /griffin/data/batch/demo_tgt/dt=${dt}/hour=${hour}
hadoop fs -touchz ${src_done_path}
hadoop fs -touchz ${tgt_done_path}
echo "insert data [$partition_date] done"
#last hour
sudo ./gen_demo_data.sh
cur_date=`date -d '1 hour ago' +%Y%m%d%H`
dt=${cur_date:0:8}
hour=${cur_date:8:2}
partition_date="dt='$dt',hour='$hour'"
sed s/PARTITION_DATE/$partition_date/ ./insert-data.hql.template > insert-data.hql
hive -f insert-data.hql
src_done_path=/griffin/data/batch/demo_src/dt=${dt}/hour=${hour}/_DONE
tgt_done_path=/griffin/data/batch/demo_tgt/dt=${dt}/hour=${hour}/_DONE
hadoop fs -mkdir -p /griffin/data/batch/demo_src/dt=${dt}/hour=${hour}
hadoop fs -mkdir -p /griffin/data/batch/demo_tgt/dt=${dt}/hour=${hour}
hadoop fs -touchz ${src_done_path}
hadoop fs -touchz ${tgt_done_path}
echo "insert data [$partition_date] done"
#next hours
set +e
while true
do
sudo ./gen_demo_data.sh
cur_date=`date +%Y%m%d%H`
next_date=`date -d "+1hour" '+%Y%m%d%H'`
dt=${next_date:0:8}
hour=${next_date:8:2}
partition_date="dt='$dt',hour='$hour'"
sed s/PARTITION_DATE/$partition_date/ ./insert-data.hql.template > insert-data.hql
hive -f insert-data.hql
src_done_path=/griffin/data/batch/demo_src/dt=${dt}/hour=${hour}/_DONE
tgt_done_path=/griffin/data/batch/demo_tgt/dt=${dt}/hour=${hour}/_DONE
hadoop fs -mkdir -p /griffin/data/batch/demo_src/dt=${dt}/hour=${hour}
hadoop fs -mkdir -p /griffin/data/batch/demo_tgt/dt=${dt}/hour=${hour}
hadoop fs -touchz ${src_done_path}
hadoop fs -touchz ${tgt_done_path}
echo "insert data [$partition_date] done"
sleep 3600
done
set -e
Then we will load data into both two tables for every hour.
/apache/data/demo$ ./gen-hive-data.sh
After a while, you can query demo data from hive table.
hive> select * from demo_src; 124 935 935 20190226 17 124 838 838 20190226 17 124 631 631 20190226 17 ...... Time taken: 2.19 seconds, Fetched: 375000 row(s)
See related data folder created on hdfs.
$ hdfs dfs -ls /griffin/data/batch drwxr-xr-x - king supergroup 0 2019-02-26 16:13 /griffin/data/batch/demo_src drwxr-xr-x - king supergroup 0 2019-02-26 16:13 /griffin/data/batch/demo_tgt $ hdfs dfs -ls /griffin/data/batch/demo_src/ drwxr-xr-x - king supergroup 0 2019-02-26 16:14 /griffin/data/batch/demo_src/dt=20190226
You need to create Elasticsearch index in advance, in order to set number of shards, replicas, and other settings to desired values:
curl -k -H "Content-Type: application/json" -X PUT http://127.0.0.1:9200/griffin \
-d '{
"aliases": {},
"mappings": {
"accuracy": {
"properties": {
"name": {
"fields": {
"keyword": {
"ignore_above": 256,
"type": "keyword"
}
},
"type": "text"
},
"tmst": {
"type": "date"
}
}
}
},
"settings": {
"index": {
"number_of_replicas": "2",
"number_of_shards": "5"
}
}
}'
You can access http://127.0.0.1:9200/griffin to verify configuration.
Everything is ready, you can login http://127.0.0.1:8080 without username and credentials. And then create measure, job to validate data quality by user guide.