This is it: a Docker multi-container environment with Hadoop (HDFS), Spark and Hive. But without the large memory requirements of a Cloudera sandbox. (On my Windows 10 laptop (with WSL2) it seems to consume a mere 3 GB.)
The only thing lacking, is that Hive server doesn't start automatically. To be added when I understand how to do that in docker-compose.
To deploy an the HDFS-Spark-Hive cluster, run:
docker-compose up
docker-compose creates a docker network that can be found by running docker network list, e.g. docker-hadoop-spark-hive_default.
Run docker network inspect on the network (e.g. docker-hadoop-spark-hive_default) to find the IP the hadoop interfaces are published on. Access these interfaces with the following URLs:
- Namenode: http://<dockerhadoop_IP_address>:9870/dfshealth.html#tab-overview
- History server: http://<dockerhadoop_IP_address>:8188/applicationhistory
- Datanode: http://<dockerhadoop_IP_address>:9864/
- Nodemanager: http://<dockerhadoop_IP_address>:8042/node
- Resource manager: http://<dockerhadoop_IP_address>:8088/
- Spark master: http://<dockerhadoop_IP_address>:8080/
- Spark worker: http://<dockerhadoop_IP_address>:8081/
- Hive: http://<dockerhadoop_IP_address>:10000
Copy breweries.csv to the namenode.
docker cp breweries.csv namenode:breweries.csv
Go to the bash shell on the namenode with that same Container ID of the namenode.
docker exec -it namenode bash
Create a HDFS directory /data//openbeer/breweries.
hdfs dfs -mkdir -p /data/openbeer/breweries
Copy breweries.csv to HDFS:
hdfs dfs -put breweries.csv /data/openbeer/breweries/breweries.csv
Go to http://<dockerhadoop_IP_address>:8080 or http://localhost:8080/ on your Docker host (laptop) to see the status of the Spark master.
Go to the command line of the Spark master and start PySpark.
docker exec -it spark-master bash
/spark/bin/pyspark --master spark://spark-master:7077
Load breweries.csv from HDFS.
brewfile = spark.read.csv("hdfs://namenode:9000/data/openbeer/breweries/breweries.csv")
brewfile.show()
+----+--------------------+-------------+-----+---+
| _c0| _c1| _c2| _c3|_c4|
+----+--------------------+-------------+-----+---+
|null| name| city|state| id|
| 0| NorthGate Brewing | Minneapolis| MN| 0|
| 1|Against the Grain...| Louisville| KY| 1|
| 2|Jack's Abby Craft...| Framingham| MA| 2|
| 3|Mike Hess Brewing...| San Diego| CA| 3|
| 4|Fort Point Beer C...|San Francisco| CA| 4|
| 5|COAST Brewing Com...| Charleston| SC| 5|
| 6|Great Divide Brew...| Denver| CO| 6|
| 7| Tapistry Brewing| Bridgman| MI| 7|
| 8| Big Lake Brewing| Holland| MI| 8|
| 9|The Mitten Brewin...| Grand Rapids| MI| 9|
| 10| Brewery Vivant| Grand Rapids| MI| 10|
| 11| Petoskey Brewing| Petoskey| MI| 11|
| 12| Blackrocks Brewery| Marquette| MI| 12|
| 13|Perrin Brewing Co...|Comstock Park| MI| 13|
| 14|Witch's Hat Brewi...| South Lyon| MI| 14|
| 15|Founders Brewing ...| Grand Rapids| MI| 15|
| 16| Flat 12 Bierwerks| Indianapolis| IN| 16|
| 17|Tin Man Brewing C...| Evansville| IN| 17|
| 18|Black Acre Brewin...| Indianapolis| IN| 18|
+----+--------------------+-------------+-----+---+
only showing top 20 rows
Go to http://<dockerhadoop_IP_address>:8080 or http://localhost:8080/ on your Docker host (laptop) to see the status of the Spark master.
Go to the command line of the Spark master and start spark-shell.
docker exec -it spark-master bash
spark/bin/spark-shell --master spark://spark-master:7077
Load breweries.csv from HDFS.
val df = spark.read.csv("hdfs://namenode:9000/data/openbeer/breweries/breweries.csv")
df.show()
+----+--------------------+-------------+-----+---+
| _c0| _c1| _c2| _c3|_c4|
+----+--------------------+-------------+-----+---+
|null| name| city|state| id|
| 0| NorthGate Brewing | Minneapolis| MN| 0|
| 1|Against the Grain...| Louisville| KY| 1|
| 2|Jack's Abby Craft...| Framingham| MA| 2|
| 3|Mike Hess Brewing...| San Diego| CA| 3|
| 4|Fort Point Beer C...|San Francisco| CA| 4|
| 5|COAST Brewing Com...| Charleston| SC| 5|
| 6|Great Divide Brew...| Denver| CO| 6|
| 7| Tapistry Brewing| Bridgman| MI| 7|
| 8| Big Lake Brewing| Holland| MI| 8|
| 9|The Mitten Brewin...| Grand Rapids| MI| 9|
| 10| Brewery Vivant| Grand Rapids| MI| 10|
| 11| Petoskey Brewing| Petoskey| MI| 11|
| 12| Blackrocks Brewery| Marquette| MI| 12|
| 13|Perrin Brewing Co...|Comstock Park| MI| 13|
| 14|Witch's Hat Brewi...| South Lyon| MI| 14|
| 15|Founders Brewing ...| Grand Rapids| MI| 15|
| 16| Flat 12 Bierwerks| Indianapolis| IN| 16|
| 17|Tin Man Brewing C...| Evansville| IN| 17|
| 18|Black Acre Brewin...| Indianapolis| IN| 18|
+----+--------------------+-------------+-----+---+
only showing top 20 rows
How cool is that? Your own Spark cluster to play with.
Go to the command line of the Hive server and start hiveserver2
docker exec -it hive-server bash
hiveserver2
Maybe a little check that something is listening on port 10000 now
netstat -anp | grep 10000
tcp 0 0 0.0.0.0:10000 0.0.0.0:* LISTEN 446/java
Okay. Beeline is the command line interface with Hive. Let's connect to hiveserver2 now.
beeline -u jdbc:hive2://localhost:10000 -n root
!connect jdbc:hive2://127.0.0.1:10000 scott tiger
Didn't expect to encounter scott/tiger again after my Oracle days. But there you have it. Definitely not a good idea to keep that user on production.
Not a lot of databases here yet.
show databases;
+----------------+
| database_name |
+----------------+
| default |
+----------------+
1 row selected (0.335 seconds)
Let's change that.
create database openbeer;
use openbeer;
And let's create a table.
CREATE EXTERNAL TABLE IF NOT EXISTS breweries(
NUM INT,
NAME CHAR(100),
CITY CHAR(100),
STATE CHAR(100),
ID INT )
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE
location '/data/openbeer/breweries';
And have a little select statement going.
select name from breweries limit 10;
+----------------------------------------------------+
| name |
+----------------------------------------------------+
| name |
| NorthGate Brewing |
| Against the Grain Brewery |
| Jack's Abby Craft Lagers |
| Mike Hess Brewing Company |
| Fort Point Beer Company |
| COAST Brewing Company |
| Great Divide Brewing Company |
| Tapistry Brewing |
| Big Lake Brewing |
+----------------------------------------------------+
10 rows selected (0.113 seconds)
There you go: your private Hive server to play with.
The configuration parameters can be specified in the hadoop.env file or as environmental variables for specific services (e.g. namenode, datanode etc.):
CORE_CONF_fs_defaultFS=hdfs://namenode:8020
CORE_CONF corresponds to core-site.xml. fs_defaultFS=hdfs://namenode:8020 will be transformed into:
<property><name>fs.defaultFS</name><value>hdfs://namenode:8020</value></property>
To define dash inside a configuration parameter, use triple underscore, such as YARN_CONF_yarn_log___aggregation___enable=true (yarn-site.xml):
<property><name>yarn.log-aggregation-enable</name><value>true</value></property>
The available configurations are:
- /etc/hadoop/core-site.xml CORE_CONF
- /etc/hadoop/hdfs-site.xml HDFS_CONF
- /etc/hadoop/yarn-site.xml YARN_CONF
- /etc/hadoop/httpfs-site.xml HTTPFS_CONF
- /etc/hadoop/kms-site.xml KMS_CONF
- /etc/hadoop/mapred-site.xml MAPRED_CONF
If you need to extend some other configuration file, refer to base/entrypoint.sh bash script.