Solr: Sharding and replication

chinaab

　　Solr allows you to create multiple search indexes, each of which is represented by a Solr core. It is possible to partition your content across multiple Solr indexes (called sharding), as well as to create multiple copies of any partition of the data (called replication).
　　Choosing to shard
　　Sharding can be useful if you have too many documents to comfortably handle on a single server.
　　

 
　　The number of shards has nothing to do with fault tolerance. It is strictly to help scale as the size of your collection of documents grows.In general, there are five primary factors you need to consider when decid-
ing on how many shards you need:

• Total number of documents
  
• Document size
  
• Required indexing throughput
  
• Query complexity
  
• Expected growth
  

　　Choosing to replicate
　　If your Solr cluster can handle 100 queries per second but your application needs to support 150 queries per second, you have a problem. Rather than breaking your index into additional partitions (adding shards), you would want to create multiple identical copies of your index and load balance traffic across each of the copies.
　　

 Master server’s solrconfig.xml

(http://masterserver:8983/solr/core1)
<requestHandler name="/replication" class="solr.ReplicationHandler">
<lst name="master">
<str name="enable">true</str>
<str name="replicateAfter">commit</str>
<str name="replicateAfter">optimize</str>
<str name="replicateAfter">startup</str>
</lst>
</requestHandler>
　　Slave server’s solrconfig.xml

(http://slaveserver:8983/solr/core1)
<requestHandler name="/replication" class="solr.ReplicationHandler">
<lst name="slave">
<str name="enable">true</str>
<str name="masterUrl">
http://masterserver:8983/solr/core1/replication
</str>
<str name="pollInterval">00:00:15</str>
</lst>
</requestHandler>
　　COMBINING SHARDING AND REPLICATION
　　At this point you know how to scale Solr to handle either more content (by sharding) or more query load (by replicating). If you are lucky enough to have both a large dataset and a large number of users trying to query your data, however, you may need to set up a cluster utilizing both sharding and replication. If you often have a large amount of indexing going, you may also want to separate your indexing operation and your query operation onto separate servers.
　　

 
　　As you can tell from figure 12.5, setting up a Solr cluster to handle both sharding and replication can quickly become a maintenance nightmare. Querying load balancing between multiple manually defined Solr cores and ensuring replication is configured and enabled between each Solr core on the slave servers and the associated Solr core on the master server can become complex quickly. If you ever have a failure in one of your nodes, it can cause multiple nodes in the cluster to fail. If the single master server in figure 12.5 fails, for example, the entire cluster will stop receiving updates. Likewise, if one slave fails, any other slaves trying to run a distributed search dependent upon the failed slave will also fail their queries.
　　
Thankfully, SolrCloud was created to take over management of these kinds of complexities for you.