Big data playground: Cluster with Hadoop, Hive, Spark, Zeppelin and Livy via Docker-compose.

I wanted to have the ability to play around with various big data applications as effortlessly as possible, namely those found in Amazon EMR. Ideally, that would be something that can be brought up and torn down in one command. This is how this repository came to be!

Constituent images:

Base image:

Zeppelin image:

Livy image:

Usage

Clone:

git clone https://github.com/panovvv/bigdata-docker-compose.git

On non-Linux platforms, you should dedicate more RAM to Docker than it does by default (2Gb on my machine with 16Gb RAM). Otherwise applications (ResourceManager in my case) will quit sporadically and you'll see messages like this one in logs:
```
current-datetime INFO org.apache.hadoop.util.JvmPauseMonitor: Detected pause in JVM or host machine (eg GC): pause of approximately 1234ms
No GCs detected
```
Increasing memory to 8G solved all those mysterious problems for me.
You should have more than 90% of free disk space, otherwise YARN will deem all nodes unhealthy.

Bring everything up:

cd bigdata-docker-compose
docker-compose up -d

data/ directory is mounted into every container, you can use this as a storage both for files you want to process using Hive/Spark/whatever and results of those computations.
livy_batches/ directory is where you have some sample code for Livy batch processing mode. It's mounted to the node where Livy is running. You can store your code there as well, or make use of the universal data/.
zeppelin_notebooks/ contains, quite predictably, notebook files for Zeppelin. Thanks to that, all your notebooks persist across runs.

Hive JDBC port is exposed to host:

URI: jdbc:hive2://localhost:10000
Driver: org.apache.hive.jdbc.HiveDriver (org.apache.hive:hive-jdbc:3.1.2)
User and password: unused.

To shut the whole thing down, run this from the same folder:

docker-compose down

Checking if everything plays well together

You can quickly check everything by opening the bundled Zeppelin notebook and running all paragraphs.

Alternatively, to get a sense of how it all works under the hood, follow the instructions below:

Hadoop and YARN:

Check YARN (Hadoop ResourceManager) Web UI (localhost:8088). You should see 2 active nodes there. There's also an alternative YARN Web UI 2 (http://localhost:8088/ui2).

Then, Hadoop Name Node UI (localhost:9870), Hadoop Data Node UIs at http://localhost:9864 and http://localhost:9865: all of those URLs should result in a page.

Open up a shell in the master node.

docker-compose exec master bash
jps

jps command outputs a list of running Java processes, which on Hadoop Namenode/Spark Master node should include those:

123 Jps
456 ResourceManager
789 NameNode
234 SecondaryNameNode
567 HistoryServer
890 Master

... but not necessarily in this order and those IDs, also some extras like RunJar and JobHistoryServer might be there too.

Then let's see if YARN can see all resources we have (2 worker nodes):

yarn node -list

current-datetime INFO client.RMProxy: Connecting to ResourceManager at master/172.28.1.1:8032
Total Nodes:2
         Node-Id	     Node-State	Node-Http-Address	Number-of-Running-Containers
   worker1:45019	        RUNNING	     worker1:8042	                           0
   worker2:41001	        RUNNING	     worker2:8042	                           0

HDFS (Hadoop distributed file system) condition:

hdfs dfsadmin -report

Live datanodes (2):
Name: 172.28.1.2:9866 (worker1)
...
Name: 172.28.1.3:9866 (worker2)

Now we'll upload a file into HDFS and see that it's visible from all nodes:

hadoop fs -put /data/grades.csv /
hadoop fs -ls /

Found N items
...
-rw-r--r--   2 root supergroup  ... /grades.csv
...

Ctrl+D out of master now. Repeat for remaining nodes (there's 3 total: master, worker1 and worker2):

docker-compose exec worker1 bash
hadoop fs -ls /

Found 1 items
-rw-r--r--   2 root supergroup  ... /grades.csv

While we're on nodes other than Hadoop Namenode/Spark Master node, jps command output should include DataNode and Worker now instead of NameNode and Master:

jps

123 Jps
456 NodeManager
789 DataNode
234 Worker

Hive

Prerequisite: there's a file grades.csv stored in HDFS ( hadoop fs -put /data/grades.csv / )

docker-compose exec master bash
hive

CREATE TABLE grades(
    `Last name` STRING,
    `First name` STRING,
    `SSN` STRING,
    `Test1` DOUBLE,
    `Test2` INT,
    `Test3` DOUBLE,
    `Test4` DOUBLE,
    `Final` DOUBLE,
    `Grade` STRING)
COMMENT 'https://people.sc.fsu.edu/~jburkardt/data/csv/csv.html'
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE
tblproperties("skip.header.line.count"="1");

LOAD DATA INPATH '/grades.csv' INTO TABLE grades;

SELECT * FROM grades;
-- OK
-- Alfalfa	Aloysius	123-45-6789	40.0	90	100.0	83.0	49.0	D-
-- Alfred	University	123-12-1234	41.0	97	96.0	97.0	48.0	D+
-- Gerty	Gramma	567-89-0123	41.0	80	60.0	40.0	44.0	C
-- Android	Electric	087-65-4321	42.0	23	36.0	45.0	47.0	B-
-- Bumpkin	Fred	456-78-9012	43.0	78	88.0	77.0	45.0	A-
-- Rubble	Betty	234-56-7890	44.0	90	80.0	90.0	46.0	C-
-- Noshow	Cecil	345-67-8901	45.0	11	-1.0	4.0	43.0	F
-- Buff	Bif	632-79-9939	46.0	20	30.0	40.0	50.0	B+
-- Airpump	Andrew	223-45-6789	49.0	1	90.0	100.0	83.0	A
-- Backus	Jim	143-12-1234	48.0	1	97.0	96.0	97.0	A+
-- Carnivore	Art	565-89-0123	44.0	1	80.0	60.0	40.0	D+
-- Dandy	Jim	087-75-4321	47.0	1	23.0	36.0	45.0	C+
-- Elephant	Ima	456-71-9012	45.0	1	78.0	88.0	77.0	B-
-- Franklin	Benny	234-56-2890	50.0	1	90.0	80.0	90.0	B-
-- George	Boy	345-67-3901	40.0	1	11.0	-1.0	4.0	B
-- Heffalump	Harvey	632-79-9439	30.0	1	20.0	30.0	40.0	C
-- Time taken: 3.324 seconds, Fetched: 16 row(s)

Ctrl+D back to bash. Check if the file's been loaded to Hive warehouse directory:

hadoop fs -ls /usr/hive/warehouse/grades

Found 1 items
-rw-r--r--   2 root supergroup  ... /usr/hive/warehouse/grades/grades.csv

The table we just created should be accessible from all nodes, let's verify that now:

docker-compose exec worker2 bash
hive

SELECT * FROM grades;

You should be able to see the same table.

Spark

Open up Spark Master Web UI (localhost:8080):

Workers (2)
Worker Id	Address	State	Cores	Memory
worker-timestamp-172.28.1.3-8882	172.28.1.3:8882	ALIVE	2 (0 Used)	1024.0 MB (0.0 B Used)
worker-timestamp-172.28.1.2-8881	172.28.1.2:8881	ALIVE	2 (0 Used)	1024.0 MB (0.0 B Used)

, also worker UIs at localhost:8081 and localhost:8082. All those pages should be accessible.

Then there's also Spark History server running at localhost:18080 - every time you run Spark jobs, you will see them here.

History Server includes REST API at localhost:18080/api/v1/applications. This is a mirror of everything on the main page, only in JSON format.

Let's run some sample jobs now:

docker-compose exec master bash
run-example SparkPi 10
#, or you can do the same via spark-submit:
spark-submit --class org.apache.spark.examples.SparkPi \
    --master yarn \
    --deploy-mode client \
    --driver-memory 2g \
    --executor-memory 1g \
    --executor-cores 1 \
    $SPARK_HOME/examples/jars/spark-examples*.jar \
    10

INFO spark.SparkContext: Running Spark version 2.4.4
INFO spark.SparkContext: Submitted application: Spark Pi
..
INFO client.RMProxy: Connecting to ResourceManager at master/172.28.1.1:8032
INFO yarn.Client: Requesting a new application from cluster with 2 NodeManagers
...
INFO yarn.Client: Application report for application_1567375394688_0001 (state: ACCEPTED)
...
INFO yarn.Client: Application report for application_1567375394688_0001 (state: RUNNING)
...
INFO scheduler.DAGScheduler: Job 0 finished: reduce at SparkPi.scala:38, took 1.102882 s
Pi is roughly 3.138915138915139
...
INFO util.ShutdownHookManager: Deleting directory /tmp/spark-81ea2c22-d96e-4d7c-a8d7-9240d8eb22ce

Spark has 3 interactive shells: spark-shell to code in Scala, pyspark for Python and sparkR for R. Let's try them all out:

hadoop fs -put /data/grades.csv /
spark-shell

spark.range(1000 * 1000 * 1000).count()

val df = spark.read.format("csv").option("header", "true").load("/grades.csv")
df.show()

df.createOrReplaceTempView("df")
spark.sql("SHOW TABLES").show()
spark.sql("SELECT * FROM df WHERE Final > 50").show()

//TODO SELECT TABLE from hive - not working for now.
spark.sql("SELECT * FROM grades").show()

Spark context Web UI available at http://localhost:4040
Spark context available as 'sc' (master = yarn, app id = application_N).
Spark session available as 'spark'.

res0: Long = 1000000000

df: org.apache.spark.sql.DataFrame = [Last name: string, First name: string ... 7 more fields]

+---------+----------+-----------+-----+-----+-----+-----+-----+-----+
|Last name|First name|        SSN|Test1|Test2|Test3|Test4|Final|Grade|
+---------+----------+-----------+-----+-----+-----+-----+-----+-----+
|  Alfalfa|  Aloysius|123-45-6789|   40|   90|  100|   83|   49|   D-|
...
|Heffalump|    Harvey|632-79-9439|   30|    1|   20|   30|   40|    C|
+---------+----------+-----------+-----+-----+-----+-----+-----+-----+

+--------+---------+-----------+
|database|tableName|isTemporary|
+--------+---------+-----------+
|        |       df|       true|
+--------+---------+-----------+

+---------+----------+-----------+-----+-----+-----+-----+-----+-----+
|Last name|First name|        SSN|Test1|Test2|Test3|Test4|Final|Grade|
+---------+----------+-----------+-----+-----+-----+-----+-----+-----+
|  Airpump|    Andrew|223-45-6789|   49|    1|   90|  100|   83|    A|
|   Backus|       Jim|143-12-1234|   48|    1|   97|   96|   97|   A+|
| Elephant|       Ima|456-71-9012|   45|    1|   78|   88|   77|   B-|
| Franklin|     Benny|234-56-2890|   50|    1|   90|   80|   90|   B-|
+---------+----------+-----------+-----+-----+-----+-----+-----+-----+

Ctrl+D out of Scala shell now.

pyspark

spark.range(1000 * 1000 * 1000).count()

df = spark.read.format('csv').option('header', 'true').load('/grades.csv')
df.show()

df.createOrReplaceTempView('df')
spark.sql('SHOW TABLES').show()
spark.sql('SELECT * FROM df WHERE Final > 50').show()

# TODO SELECT TABLE from hive - not working for now.
spark.sql('SELECT * FROM grades').show()

1000000000

$same_tables_as_above

Ctrl+D out of PySpark.

sparkR

df <- as.DataFrame(list("One", "Two", "Three", "Four"), "This is as example")
head(df)

df <- read.df("/grades.csv", "csv", header="true")
head(df)

  This is as example
1                One
2                Two
3              Three
4               Four

$same_tables_as_above

Amazon S3

From Hadoop:

hadoop fs -Dfs.s3a.impl="org.apache.hadoop.fs.s3a.S3AFileSystem" -Dfs.s3a.access.key="classified" -Dfs.s3a.secret.key="classified" -ls "s3a://bucket"

Then from PySpark:

sc._jsc.hadoopConfiguration().set('fs.s3a.impl', 'org.apache.hadoop.fs.s3a.S3AFileSystem')
sc._jsc.hadoopConfiguration().set('fs.s3a.access.key', 'classified')
sc._jsc.hadoopConfiguration().set('fs.s3a.secret.key', 'classified')

df = spark.read.format('csv').option('header', 'true').option('sep', '\t').load('s3a://bucket/tabseparated_withheader.tsv')
df.show(5)

None of the commands above stores your credentials anywhere (i.e. as soon as you'd shut down the cluster your creds are safe). More persistent ways of storing the credentials are out of scope of this readme.

Zeppelin

Zeppelin interface should be available at http://localhost:8890.

You'll find a notebook called "test" in there, containing commands to test integration with bash, Spark and Livy.

Livy

Livy is at http://localhost:8998 (and yes, there's a web UI as well as REST API on that port - just click the link).

Livy Sessions.

Try to poll the REST API:

curl --request GET \
  --url http://localhost:8998/sessions | python3 -mjson.tool

The response, assuming you didn't create any sessions before, should look like this:

{
  "from": 0,
  "total": 0,
  "sessions": []
}

1 ) Create a session:

curl --request POST \
  --url http://localhost:8998/sessions \
  --header 'content-type: application/json' \
  --data '{
	"kind": "pyspark"
}' | python3 -mjson.tool

Response:

{
    "id": 0,
    "name": null,
    "appId": null,
    "owner": null,
    "proxyUser": null,
    "state": "starting",
    "kind": "pyspark",
    "appInfo": {
        "driverLogUrl": null,
        "sparkUiUrl": null
    },
    "log": [
        "stdout: ",
        "\nstderr: ",
        "\nYARN Diagnostics: "
    ]
}

2 ) Wait for session to start (state will transition from "starting" to "idle"):

curl --request GET \
  --url http://localhost:8998/sessions/0 | python3 -mjson.tool

Response:

{
    "id": 0,
    "name": null,
    "appId": "application_1584274334558_0001",
    "owner": null,
    "proxyUser": null,
    "state": "starting",
    "kind": "pyspark",
    "appInfo": {
        "driverLogUrl": "http://worker2:8042/node/containerlogs/container_1584274334558_0003_01_000001/root",
        "sparkUiUrl": "http://master:8088/proxy/application_1584274334558_0003/"
    },
    "log": [
        "timestamp bla"
    ]
}

3 ) Post some statements:

curl --request POST \
  --url http://localhost:8998/sessions/0/statements \
  --header 'content-type: application/json' \
  --data '{
	"code": "import sys;print(sys.version)"
}' | python3 -mjson.tool
curl --request POST \
  --url http://localhost:8998/sessions/0/statements \
  --header 'content-type: application/json' \
  --data '{
	"code": "spark.range(1000 * 1000 * 1000).count()"
}' | python3 -mjson.tool

Response:

{
    "id": 0,
    "code": "import sys;print(sys.version)",
    "state": "waiting",
    "output": null,
    "progress": 0.0,
    "started": 0,
    "completed": 0
}

{
    "id": 1,
    "code": "spark.range(1000 * 1000 * 1000).count()",
    "state": "waiting",
    "output": null,
    "progress": 0.0,
    "started": 0,
    "completed": 0
}

Get the result:

curl --request GET \
  --url http://localhost:8998/sessions/0/statements | python3 -mjson.tool

Response:

{
  "total_statements": 2,
  "statements": [
    {
      "id": 0,
      "code": "import sys;print(sys.version)",
      "state": "available",
      "output": {
        "status": "ok",
        "execution_count": 0,
        "data": {
          "text/plain": "3.7.3 (default, Apr  3 2019, 19:16:38) \n[GCC 8.0.1 20180414 (experimental) [trunk revision 259383]]"
        }
      },
      "progress": 1.0
    },
    {
      "id": 1,
      "code": "spark.range(1000 * 1000 * 1000).count()",
      "state": "available",
      "output": {
        "status": "ok",
        "execution_count": 1,
        "data": {
          "text/plain": "1000000000"
        }
      },
      "progress": 1.0
    }
  ]
}

Delete the session:

curl --request DELETE \
  --url http://localhost:8998/sessions/0 | python3 -mjson.tool

Response:

{
  "msg": "deleted"
}

Livy Batches.

To get all active batches:

curl --request GET \
  --url http://localhost:8998/batches | python3 -mjson.tool

Strange enough, this elicits the same response as if we were querying the sessions endpoint, but ok...

1 ) Send the batch:

curl --request POST \
  --url http://localhost:8998/batches \
  --header 'content-type: application/json' \
  --data '{
	"file": "local:/data/batches/sample_batch.py",
	"pyFiles": [
		"local:/data/batches/sample_batch.py"
	],
	"args": [
		"123"
	]
}' | python3 -mjson.tool

Response:

{
    "id": 0,
    "name": null,
    "owner": null,
    "proxyUser": null,
    "state": "starting",
    "appId": null,
    "appInfo": {
        "driverLogUrl": null,
        "sparkUiUrl": null
    },
    "log": [
        "stdout: ",
        "\nstderr: ",
        "\nYARN Diagnostics: "
    ]
}

2 ) Query the status:

curl --request GET \
  --url http://localhost:8998/batches/0 | python3 -mjson.tool

Response:

{
    "id": 0,
    "name": null,
    "owner": null,
    "proxyUser": null,
    "state": "running",
    "appId": "application_1584274334558_0005",
    "appInfo": {
        "driverLogUrl": "http://worker2:8042/node/containerlogs/container_1584274334558_0005_01_000001/root",
        "sparkUiUrl": "http://master:8088/proxy/application_1584274334558_0005/"
    },
    "log": [
        "timestamp bla",
        "\nstderr: ",
        "\nYARN Diagnostics: "
    ]
}

3 ) To see all log lines, query the /log endpoint. You can skip 'to' and 'from' params, or manipulate them to get all log lines. Livy (as of 0.7.0) supports no more than 100 log lines per response.

curl --request GET \
  --url 'http://localhost:8998/batches/0/log?from=100&to=200' | python3 -mjson.tool

Response:

{
    "id": 0,
    "from": 100,
    "total": 203,
    "log": [
        "...",
        "Welcome to",
        "      ____              __",
        "     / __/__  ___ _____/ /__",
        "    _\\ \\/ _ \\/ _ `/ __/  '_/",
        "   /__ / .__/\\_,_/_/ /_/\\_\\   version 2.4.5",
        "      /_/",
        "",
        "Using Python version 3.7.5 (default, Oct 17 2019 12:25:15)",
        "SparkSession available as 'spark'.",
        "3.7.5 (default, Oct 17 2019, 12:25:15) ",
        "[GCC 8.3.0]",
        "Arguments: ",
        "['/data/batches/sample_batch.py', '123']",
        "Custom number passed in args: 123",
        "Will raise 123 to the power of 3...",
        "...",
        "123 ^ 3 = 1860867",
        "...",
        "2020-03-15 13:06:09,503 INFO util.ShutdownHookManager: Deleting directory /tmp/spark-138164b7-c5dc-4dc5-be6b-7a49c6bcdff0/pyspark-4d73b7c7-e27c-462f-9e5a-96011790d059"
    ]
}

4 ) Delete the batch:

curl --request DELETE \
  --url http://localhost:8998/batches/0 | python3 -mjson.tool

Response:

{
  "msg": "deleted"
}

Credits

Sample data file:

grades.csv is borrowed from John Burkardt's page under Florida State University domain. Thanks for sharing those!
ssn-address.tsv is derived from grades.csv by removing some fields and adding randomly-generated addresses.

panovvv/bigdata-docker-compose

panovvv

Reviews

Repository Details

Big data playground: Cluster with Hadoop, Hive, Spark, Zeppelin and Livy via Docker-compose.

Constituent images:

Usage

Checking if everything plays well together

Hadoop and YARN:

Hive

Spark

Zeppelin

Livy

Credits

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