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Wikipedia Graph Database Deployment Tutorial

This deployment tutorial is for Linux users. If you want to know how to deploy the database on MacOS, please create an issue in the GitHub repository of the project.

1. Set up the environment

  1. Install JDK. Open JDK 8 recommended.
  2. Install SBT (Scala Build Tool)
  3. Install Apache Spark
    • We recommend using Apache Bigtop package manager to install Spark. Here is a step-by-step tutorial on how to do this. This option has been tested on Linux only.
    • Another way is to follow the steps described on the Apache Spark website.
  4. Install Neo4J (if you need to work with Wikipedia graph).
  5. Install Apache Cassandra (if you need to work with pagecounts).
  6. Build the project.
    • Clone the repository from GitHub. The code prepares the data for the deployment.
    • Run sbt package to build the project and get the .jar file. We will need this file to run pre-processing jobs in Spark.

2. Download the data

For the sake of simplicity of this tutorial, let's say the local path to the data on your machine is the following:


We are going to use it in all the deployment commands. Change the path in the commands accordingly when you run this tutorial on your machine.

2.1 SQL dumps

We will need the dumps to build Wikipedia graph.

  1. Download SQL dumps here. No need to download all the dumps though. Download only page, redirect, category, categorylinks, and pagelinks dumps:

    • enwiki-YYYYMMDD-page.sql.gz
    • enwiki-YYYYMMDD-redirect.sql.gz
    • enwiki-YYYYMMDD-category.sql.gz
    • enwiki-YYYYMMDD-categorylinks.sql.gz
    • enwiki-YYYYMMDD-pagelinks.sql.gz
  2. Convert the *.sql.gz archives to * for performance reasons (s.t. Spark can process .bz archives in parallel). You can use the following comand to do this: zcat dump.sql.gz | bzip2 > dump.sql.gz

  3. Put the files to /mnt/data/wikipedia/dumps/.

2.2 Pagecounts

  1. Download pagecounts archive here. For example, if you need the data for 2019, choose the corresponding folder and download the data for the month(s) of your interest.

    Important note: if you download archives of multiple months/years, put them in one folder (do not create separate folders for different months/years).

  2. If you have already downloaded the SQL dumps described in the previous section, skip this step. Download enwiki-YYYYMMDD-page.sql.gz SQL dump here.

  3. Put the files to /mnt/data/wikipedia/pagecounts/.

3. Deploy the graph database

3.1 Pre-process files

To pre-process the dumps we will need to run spark-submit command in the following format:

--class ch.epfl.lts2.wikipedia.DumpProcessor 
--master 'local[*]' 
--executor-memory [amount of RAM allocated for executor (30% of available RAM)] 
--driver-memory [amount of RAM allocated for driver (40% of available RAM)]
--packages  org.rogach:scallop_2.11:3.1.5,
            [path to the *.jar file that we have build in the first section] 
--dumpPath [path to raw SQL dumps]
--outputPath [output path] 
--namePrefix enwiki-[YYYYMMDD]


spark-submit --class ch.epfl.lts2.wikipedia.DumpProcessor --master 'local[*]' --executor-memory 4g --driver-memory 4g --packages org.rogach:scallop_2.11:3.1.5,com.datastax.spark:spark-cassandra-connector_2.11:2.4.0 sparkwiki/target/scala-2.11/sparkwiki_2.11-0.8.5.jar --dumpPath /mnt/data/Datasets/wikipedia/dumps --outputPath /mnt/data/wikipedia/dumps-pre-processed/  --namePrefix enwiki-20180801

After running this command you will see Spark logs in the terminal. After some time (around 30 minutes), you will have pre-processed SQL dumps stored in /mnt/data/wikipedia/dumps-pre-processed/.

3.2 Create wikipedia.db database

The default database in Neo4J is graph.db. We will need to create another database and call it wikipedia.db. To do that we will need to change the config file. Open /etc/neo4j/neo4j.conf and edit+uncomment the following line:


After that, once you restart Neo4J service, wikipedia.db database will be created.

3.3 Start/Stop Neo4J

Start/Stop Neo4J service to initialize the wikipedia.db.

sudo neo4j start

sudo neo4j stop

3.4 Import the pre-processed files in Neo4J

Note: Neo4J service should be down. Otherwise, the script will not work.

  • Run the script below to import the pre-processed files into Neo4J. This step takes quite some time depending on your hardware (the amount of RAM and the type of the storage). For example, on a computer with 32 GB of RAM and an SSD (free space of around 10 GB required), it should take less than 30 minutes.
#! /bin/sh
neo4j-admin import \
    --database=$target_db --delimiter=$delim \
    --report-file=/tmp/import-wiki.log \
    --id-type=INTEGER \
    --nodes:Page import/page_header.csv,"$data_dir/page/normal_pages/$part_template" \
    --nodes:Page:Category import/page_header.csv,"$data_dir/page/category_pages/$part_template" \
    --relationships:LINKS_TO import/pagelinks_header.csv,"$data_dir/pagelinks/$part_template" \
    --relationships:BELONGS_TO import/categorylinks_header.csv,"$data_dir/categorylinks/$part_template" \

  • Start Neo4J database service. sudo neo4j start

  • Open Neo4J web interface. http://localhost:7474/browser/

  • Create indexes for PAGE_ID and PAGE_TITLE.

CREATE INDEX ON :Page(title)
  • Check the indexes are complete.
CALL db.indexes()
  • Test some queries. Example: MATCH (p)-[:BELONGS_TO*1..2]->(c:Category { title: 'Physics'}) WITH DISTINCT p AS p1 RETURN, p1.title, labels(p1);

4. Deploy the pagecounts database

4.1 Start Apache Cassandra

sudo service cassandra start

4.2 Check IP of your Cassandra node

nodetool status

4.3 Open Cassandra console


4.4 Create keyspace

The query below will create a keyspace for a single-node environment.

CREATE KEYSPACE wikipedia WITH REPLICATION = { 'class' : 'SimpleStrategy', 'replication_factor' : 1 };

If you want to configure a multi-node environment, read more about replication strategies here.

4.5 Create tables to import pagecounts

CREATE TABLE wikipedia.page_visits ( page_id bigint, visit_time timestamp, count int, PRIMARY KEY (page_id, visit_time));

CREATE TABLE wikipedia.pagecount_metadata ( start_time timestamp, end_time timestamp, PRIMARY KEY (start_time, end_time));

4.6 Exit cqlsh

4.7 Pre-process raw pagecounts

Use ch.epfl.lts2.wikipedia.DumpParser to get .parquet files for the page.sql dumps. To do this, run a command in the following format:

For more information on the parameters check out the README.

--class ch.epfl.lts2.wikipedia.DumpParser 
--master 'local[*]' 
--executor-memory 10g 
--driver-memory 10g 
--packages    org.rogach:scallop_2.11:3.1.5,
              <SPARKWIKI LOCATION>/sparkwiki/target/scala-2.11/sparkwiki_2.11-0.8.5.jar 
--dumpFilePath /mnt/data/wikipedia/dumps/enwiki-<DATE>-page.sql.bz2 
--dumpType page 
--outputPath /mnt/data/wikipedia/page.parquet 
--outputFormat parquet

4.8 Import the pagecounts files into Cassandra

Use ch.epfl.lts2.wikipedia.PagecountProcessor to import the files.

For more information on the parameters check out the README.

--class ch.epfl.lts2.wikipedia.PagecountProcessor 
--master 'local[*]' 
--executor-memory 10g 
--driver-memory 10g 
--packages     org.rogach:scallop_2.11:3.1.5,
               <SPARKWIKI LOCATION>/sparkwiki/target/scala-2.11/sparkwiki_2.11-0.8.6.jar
--config <SPARKWIKI LOCATION>/sparkwiki/config/pagecount.conf
--basePath /mnt/data/wikipedia/pagecounts/2018/2018-08
--startDate 2018-08-01
--endDate 2018-08-31
--pageDump /mnt/data/wikipedia/page.parquet

4.9 Verify the import. Show the table with pagecounts

sudo service cassandra start


select * from wikipedia.page_visits limit 10;


sudo service cassandra stop

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