How do you scale Solr?

How do you scale Solr?

The standard procedure for scaling Lucene/Solr is as follows: first, maximize performance on a single machine. Next, absorb high query volume by replicating to multiple machines. If the index becomes too large for a single machine, split the index across multiple machines (or, shard the index).

How to improve Solr performance?

Solr uses direct memory to cache data read from disks, mostly index, to improve performance. Direct memory doesn’t cause any JVM GC overhead. Direct memory size can be set using CM (Cloudera Manager->Solr configuration->direct memory).

How is Solr performance measured?

Key Solr Metrics to Monitor

1. Request Rate. Each handler in Solr provides information about the rate of the requests that are sent to it.
2. Request Latency.
3. Commit Events.
4. Caches Utilization.
5. CPU Utilization.
6. Memory, JVM Memory & Swap.
7. Disk Utilization.
8. Garbage Collector Statistics.

How does Solr cache work?

Solr caches are associated with a specific instance of an Index Searcher, a specific view of an index that doesn’t change during the lifetime of that searcher. As long as that Index Searcher is being used, any items in its cache will be valid and available for reuse.

How many requests can SOLR handle?

The expected size of documents in index in about 200 Million and each document can have maximum 20 fields. Most fields are both indexed and stored. The expected simultaneous requests can be hundreds of thousands at a time.

What is JVM memory SOLR?

solr java. Sometimes in the development or other environments (QA,STG,PROD), it is required to increase the size of JVM (Java Virtual Machine) memory.

How do I disable Solr cache?

Solr caches the results of all queries and filter queries by default. To disable result caching, set the cache=false parameter. You can also use the cost option to control the order in which non-cached filter queries are evaluated.

How big can a Lucene Solr index be?

Depending on a multitude of factors, a single machine can easily host a Lucene/Solr index of 5 – 80+ million documents, while a distributed solution can provide subsecond search response times across billions of documents. Over that range, query throughput can be adjusted with index replication at each individual server.

How does scaling in Solr start and end?

In the Scaling Progression diagram, you can get a better visual idea of how this progression works. It starts with a single machine serving all queries and handling all index updates. Next there is the master/slave configuration, where the master handles all updates and replicates all index changes to the slaves.

What kind of fieldcache is used in Solr?

Lucene uses FieldCache to efficiently access all of the values for a field in memory rather than going to disk. This is necessary for sorting and can be used for Solr’s faceting, among other things.

Which is a scalable search engine Lucene or Solr?

Lucene and Solr are both highly scalable search solutions. Depending on a multitude of factors, a single machine can easily host a Lucene/Solr index of 5 – 80+ million documents, while a distributed solution can provide subsecond search response times across billions of documents.