What is SolrCloud? (And how does it compare to master-slave?)

What is SolrCloud?

(And how does it compare to master-slave?)

SolrCloud is a set of new features and functionality added in Solr 4.0 to enable a new way of creating durable, highly available Solr clusters with commodity hardware. While similar in many ways to master-slave, SolrCloud automates a lot of the manual labor required in master-slave through using ZooKeeper nodes to monitor the state of the cluster as well as additional Solr features that understand how to interact with other machines in their Solr cluster. 

In short, where master-slave requires manual effort to change the role of a node in a given cluster and to add new nodes to a cluster, SolrCloud aims to automate a lot of that work and allow seamless addition of new nodes to a cluster and to work around downed nodes with minimal oversight.

What does a SolrCloud look like compared to master-slave?

On the surface, the primary difference between SolrCloud and master-slave is the additional requirement of at least three ZooKeeper (ZK) nodes. Effectively, this means the minimum size of a SolrCloud cluster is larger than for master-slave, but the ZK nodes do not need to be particularly powerful. As their only role is to monitor and maintain the state of nodes in the SolrCloud, latency is more important than computing power so the ZooKeeper nodes can be fairly minimal machines, so long as they are dedicated to the purpose.


Why would I need SolrCloud or master-slave at all?

SolrCloud and master-slave both address four particular issues:

  • Sharding
  • Near Real Time (NRT) search and incremental indexing
  • Query distribution and load balancing
  • High Availability (HA)

What is sharding?

Sharding is the act of splitting a single Solr index across multiple machines, also known as shards or slices in Solr terminology. Sharding is most often needed because an index has grown too large to fit on a single server. A given shard can contain multiple physical/virtual servers, meaning that all the machines/replicas on that shard contain the same index data and serve queries for that data. 

Through sharding, you can split your index across multiple machines and continue to grow without running into problems. More information can be found here: http://docs.lucidworks.com/display/solr/Shards+and+Indexing+Data+in+SolrCloud

Near Real Time search and incremental indexing:

Master-slave operates with every shard having a single master which takes care of all indexing. All other nodes in the shard are slaves, and upon entering the shard are given the current state of the index using SolrReplication (http://wiki.apache.org/solr/SolrReplication). Once a slave has been replicated, the slave's pollInterval determines how often it will contact the shard master to receive index updates.

SolrCloud similarly has a leader in every shard but the leader is largely the same as any other replica, both indexing documents and serving queries. The only additional responsibility of the leader is to distribute documents to be indexed to all other replicas in the shard, and to then report that all replicas have confirmed receiving a given document. Any document sent into SolrCloud is re-routed to the leader of the appropriate shard, who then performs this responsibility. When a replica receives a document, it adds the document to its transaction log and it will send a response to the leader. In this way, updates to SolrCloud indexes are performed in a distributed manner and are durable.

Once a document has been added to the transaction logs, it is available via a RealTimeGet (http://wiki.apache.org/solr/RealTimeGet), but is not available via search until a soft commit or hard commit with openSearcher=true has been executed. A manual soft commit or hard commit will make all documents in the transaction log available for search. One can also use the autoCommit and autoSoftCommit parameters to trigger commits from individual nodes on a regular basis.

Query distribution and load balancing:

In master-slave, querying a single node will only bring you results from that node, which in most cases is equivalent to querying one slice of your data. In order to generate a query for your entire sharded index, you must use a Distributed Search (http://wiki.apache.org/solr/DistributedSearch) to query one node per shard. In the event that one of those nodes is unable to respond, an error will be given and the query will not be fulfilled. This being the case, trying to load balance queries across a master-slave cluster can be problematic. Master-slave also does not provide any load balancing, and thus requires an external load balancer.

With SolrCloud, distributed searching is handled automatically by the nodes in the cloud: querying any node will cause that node to send the query out to one node in all other shards, returning a response only when it has aggregated the results from all shards. Furthermore, ZooKeeper and all replicas are aware of any non-responding nodes, and therefore won't direct queries to nodes that are considered dead. In the event that a downed node has not yet been detected by ZK and is sent a query, the querying node will report the node as down to ZK, and resend the query to another node. In this way, queries to a SolrCloud are quite durable and will almost never be interrupted by a downed node.

SolrCloud can handle its own load balancing if you use a smart client such as Solrj (http://wiki.apache.org/solr/Solrj). Solrj will use a simple round robin load balancer, distributing queries evenly to all nodes in SolrCloud. Furthermore, Solrj is ZooKeeper aware and thus will never send a query to a node that is known as down.

High Availability:

In the event of a downed master in a master-slave cluster, the shard can continue to serve queries, but will no longer be able to index until a new master is instated. The process of promoting a slave to a master is manual, though it can be scripted. Any updates to the master's index since the last replication are lost, and those documents will have to be resubmitted. When a slave disconnects from the cluster and then rejoins, it will automatically retrieve any missed updates/index segments from the master before it is considered ready to serve queries.

In SolrCloud, when ZooKeeper detects a leader has gone down, it will initiate the leader election process instantaneously, selecting a new leader to begin distributing documents again. Since the transaction log ensures that all nodes in the shard are in sync, all updates are durable and never lost when a leader goes down. Similar to master-slave, when a replica rejoins the cluster it simply replays the transaction log to bring itself up to date with other machines in the shard. In some cases if a replica has missed too many updates, it will perform a standard replication as well as replaying the transaction log before serving queries.

Both SolrCloud and master-slave take advantage of SolrReplication, but SolrCloud automatically handles rerouting and recovery when any node in the cluster goes down, whereas master-slave requires some manual work in the event a master becomes unresponsive. As a result, turning a master-slave cluster into a HA solution requires a fair amount of work in scripting and case checking, to ensure no documents are lost and that queries are accurate. By contrast, SolrCloud will never lose updates and will automatically route around any unresponsive nodes. 

See also: https://support.lucidworks.com/entries/22180608-Solr-HA-DR-overview-3-x-and-4-0-SolrCloud-

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  • 0

    Hi, this was helpful for  understanding solr cloud. But we have an issue here.


    We have a solr cloud with one shard and four machines in it as nodes, lets say, solr1, solr2, solr3, solr4. Right now solr2 is leader. We are commiting records to solr1, we notice that any record which we commit is replicating to solr2, solr4 immediately, however, the same record is coming to solr3 after 10 minutes. Please let us know if its a configuration issue. 

    Also this behaviour changes when we restart the cluster

    so if X was lagging after restart it may not lag again



    Manoj Reddy T.


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