Skip to content

Latest commit

 

History

History
76 lines (40 loc) · 2.14 KB

README.md

File metadata and controls

76 lines (40 loc) · 2.14 KB
Raw

solr-map-reduce-example

This project is meant to provide an example of how to build Solr indexes with MapReduce.

The main part is the script called run-example. This script will download Hadoop and Solr and start both of them. It will then run a map-reduce job and build an index from the included sample twitter data. Finally, the indexes will be deployed to the Solr cluster via the GoLive feature.

The script is meant as both a way to quickly see something working and as a reference for building Solr indexes on your real Hadoop cluster.

This is not an example of good production settings! This setup is meant for a single node demo system.

Running the Example

Download the repository files using the 'Download ZIP' button and extract them to a new directory. From that directory, run the included run-example.sh script and sit back and watch.

  bash run-example.sh

Stopping the Example

Run the included stop script.

  bash stop-example.sh

Files

run-example.sh - the main script that downloads and runs everything

stop-example.sh - a script to stop the services started by run-example.sh

log4j.properties - the log4j config file attached to the map-reduce job

readAvroContainer.conf - a Morphline for reading avro files

sample-statuses-20120906-141433-medium.avro - sample Twitter format data

schema.xml - a schema for the sample Twitter formated data

Software Versions

This is currently using:

Hadoop 2.6.0

Solr 5.2.1

Web URLs

Solr http://127.0.0.1:8983/solr NameNode http://127.0.0.1:50075 Yarn http://127.0.0.1:8042

Links

Running Solr on HDFS - https://cwiki.apache.org/confluence/display/solr/Running+Solr+on+HDFS

Morphlines - http://kitesdk.org/docs/current/kite-morphlines/index.html

Errata

Most logs and files will be created in /tmp/solr-map-reduce - anything not found there should be under the Solr-map-reduce-example directory itself.

There are two inefficient waits in the script - after we start HDFS and after we start Solr, we naively wait.