When your collection is too large for one node, you can break it up and store it in sections by creating multiple shards.
A Shard is a logical partition of the collection, containing a subset of documents from the collection, such that every document in a collection is contained in exactly one Shard. Which shard contains a each document in a collection depends on the overall "Sharding" strategy for that collection. For example, you might have a collection where the "country" field of each document determines which shard it is part of, so documents from the same country are co-located. A different collection might simply use a "hash" on the uniqueKey of each document to determine its Shard.
Before SolrCloud, Solr supported Distributed Search, which allowed one query to be executed across multiple shards, so the query was executed against the entire Solr index and no documents would be missed from the search results. So splitting an index across shards is not exclusively a SolrCloud concept. There were, however, several problems with the distributed approach that necessitated improvement with SolrCloud:
- Splitting an index into shards was somewhat manual.
- There was no support for distributed indexing, which meant that you needed to explicitly send documents to a specific shard; Solr couldn't figure out on its own what shards to send documents to.
- There was no load balancing or failover, so if you got a high number of queries, you needed to figure out where to send them and if one shard died it was just gone.
SolrCloud fixes all those problems. There is support for distributing both the index process and the queries automatically, and ZooKeeper provides failover and load balancing. Additionally, every shard can also have multiple replicas for additional robustness.
In SolrCloud there are no masters or slaves. Instead, every shard consists of at least one physical replica, exactly one of which is a leader. Leaders are automatically elected, initially on a first-come-first-served basis, and then based on the Zookeeper process described at http://zookeeper.apache.org/doc/trunk/recipes.html#sc_leaderElection..
If a leader goes down, one of the other replicas is automatically elected as the new leader.
When a document is sent to a Solr node for indexing, the system first determines which Shard that document belongs to, and then which node is currently hosting the leader for that shard. The document is then forwarded to the current leader for indexing, and the leader forwards the update to all of the other replicas.
Solr offers the ability to specify the router implementation used by a collection by specifying the
router.name parameter when creating your collection. If you use the (default) "
compositeId" router, you can send documents with a prefix in the document ID which will be used to calculate the hash Solr uses to determine the shard a document is sent to for indexing. The prefix can be anything you'd like it to be (it doesn't have to be the shard name, for example), but it must be consistent so Solr behaves consistently. For example, if you wanted to co-locate documents for a customer, you could use the customer name or ID as the prefix. If your customer is "IBM", for example, with a document with the ID "12345", you would insert the prefix into the document id field: "IBM!12345". The exclamation mark ('!') is critical here, as it distinguishes the prefix used to determine which shard to direct the document to.
Then at query time, you include the prefix(es) into your query with the
_route_ parameter (i.e.,
q=solr&_route_=IBM!) to direct queries to specific shards. In some situations, this may improve query performance because it overcomes network latency when querying all the shards.
_route_ parameter replaces
shard.keys, which has been deprecated and will be removed in a future Solr release.
compositeId router supports prefixes containing up to 2 levels of routing. For example: a prefix routing first by region, then by customer: "USA!IBM!12345"
Another use case could be if the customer "IBM" has a lot of documents and you want to spread it across multiple shards. The syntax for such a use case would be : "shard_key/num!document_id" where the /num is the number of bits from the shard key to use in the composite hash.
So "IBM/3!12345" will take 3 bits from the shard key and 29 bits from the unique doc id, spreading the tenant over 1/8th of the shards in the collection. Likewise if the num value was 2 it would spread the documents across 1/4th the number of shards. At query time, you include the prefix(es) along with the number of bits into your query with the
_route_ parameter (i.e.,
q=solr&_route_=IBM/3!) to direct queries to specific shards.
If you do not want to influence how documents are stored, you don't need to specify a prefix in your document ID.
If you created the collection and defined the "implicit" router at the time of creation, you can additionally define a
router.field parameter to use a field from each document to identify a shard where the document belongs. If the field specified is missing in the document, however, the document will be rejected. You could also use the
_route_ parameter to name a specific shard.
When you create a collection in SolrCloud, you decide on the initial number shards to be used. But it can be difficult to know in advance the number of shards that you need, particularly when organizational requirements can change at a moment's notice, and the cost of finding out later that you chose wrong can be high, involving creating new cores and re-indexing all of your data.
The ability to split shards is in the Collections API. It currently allows splitting a shard into two pieces. The existing shard is left as-is, so the split action effectively makes two copies of the data as new shards. You can delete the old shard at a later time when you're ready.
More details on how to use shard splitting is in the section on the Collection API's SPLITSHARD command.
Ignoring Commits from Client Applications in SolrCloud
In most cases, when running in SolrCloud mode, indexing client applications should not send explicit commit requests. Rather, you should configure auto commits with
openSearcher=false and auto soft-commits to make recent updates visible in search requests. This ensures that auto commits occur on a regular schedule in the cluster. To enforce a policy where client applications should not send explicit commits, you should update all client applications that index data into SolrCloud. However, that is not always feasible, so Solr provides the IgnoreCommitOptimizeUpdateProcessorFactory, which allows you to ignore explicit commits and/or optimize requests from client applications without having refactor your client application code. To activate this request processor you'll need to add the following to your solrconfig.xml:
As shown in the example above, the processor will return 200 to the client but will ignore the commit / optimize request. Notice that you need to wire-in the implicit processors needed by SolrCloud as well, since this custom chain is taking the place of the default chain.
In the following example, the processor will raise an exception with a 403 code with a customized error message:
Lastly, you can also configure it to just ignore optimize and let commits pass thru by doing: