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Apache Solr Cloud Architecture

High availability and fault tolerance are combined with Solr server,  we called it as Solr cloud. It provides distributed indexing  and searching  capabilities.

The most important features of  Solr cloud,

  • Central configuration for every cluster
  • Automatic load balancing and failover for queries
  • Zoo Keeper integration for cluster coordination and configuration.

Apache Solr Cloud Architecture



Solr is helps to enable the subset of optional features and also simplifying  horizontal scaling a search  index using shard and replicating. Solr  distributed cloud is mainly via distributed indexing side. Single Solr server modes are really fast and it has more features. High scalability. It counts Number of  requests comes and updates the queries  come. If we reached a maximum size of  a single server, we have to add  another server, so Solr designing cloud. Procedure to add the other server is explained below. Put some documents to another server, it allows the replicate the data.

Leader: A node that can accept writes without consulting another node. So, any node is basically a leader they determined any latency and accepting requests are  updating.

Is everyone a Leader?

  • Favors write availability
  • Challenges optimistic locking
  • Challenges consistency

Favors write availability: We need only any given node to be up in its going to accept our right arm to the downside to it makes.

Challenges optimistic locking: Optimistic locking more difficult, so when  everybody’s leader what’s nice is if our notes get partitioned so they have got 170 is over, if  both clusters except  right, so if we want to do  something like optimistic locking, it sending  an update and we say which version we are trying to update and we should confirm whether we could update it  or not. If we have separate partitions, it’s not easy to get information back immediately, because one partition and the other partition may be the one that has the document, met our trying update  and its come back together.

Challenges consistency: We know units it’s an eventual consistency model. Primary reasons to offers optimistic locking is a form of transaction in their we can have form atomic updates on its per single document and a transaction that involves multiple documents.

Collections: Collection made of one or more Solr cores, single core contains single  Solr instances, Collection of shard1 and shard2. Each of  the shard is placed on two Solr instances.

Zoo Keeper:  It is a vital part of  Solr cloud. If  the Zoo  Keeper will fail the whole cluster become useless Leader election, cluster state management and centralized configuration are provided by  the distributed coordination service. We can use the embedded Zoo Keeper for testing.

Collection:  Each  collection has a name, shard count and replication factor  in distributed search index across multiple nodes.

Replication  factor: Number of copies of  a document in a collection.

Shard: It is a collection of logical slices, While the ability to shard a logical Solr index is an excellent feature.

Every  shard has a name, leader, hash range and replication factor. Each Shard will contain at least one Leader Core and zero to many Replica Cores, One document is assigned to one shard per collection using a hash based  document routing strategy.

Replica: It is a Core that stores a copy of a Leader Core’s index, each replica is implemented by Solr core. Replica Cores and other Leader Cores are dependable for forwarding the Solr Document to the appropriate Leader Core.

This blog will help you get a better understanding of Solr + Hadoop = Big Data Love!

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