codecentric AG Patrick Peschlow Elasticsearch Lessons Learned 

codecentric AG Beginner’s lesson Quick introduction 

codecentric AG Lessons learned Map carefully 

codecentric AG Map carefully − Disable the _all field, you definitely don’t want it ! − Keep the _source field enabled and don’t set any fields to _stored ! − Disable dynamic mapping (except where really needed) ! − Choose analyzers carefully (for many fields, „not_analyzed“ might be enough) ! − Consider mapping fields more than once ! − Existing mappings cannot be changed without deleting the type 

codecentric AG Lessons learned Understand filters 

codecentric AG Understand filters − Use filters instead of queries whenever you don’t need scoring − Elasticsearch is able to cache the results of filters ! − Tricky caching behavior − Most simple filters are cached by default, but some not (e.g., geo) − Compound filters (bool/and/or/not) are not cached − You can still explicitly request caching by setting _cache − Bool filters query the cache for their (sub-)filters, but and/or/not filters don’t ! − But: This topic seems to be a moving target ! − Consider the scope of filters − Apply to query, facets/aggregations, or both? − Often „filtered query“ is what you need 

codecentric AG Lessons learned Use analysis tooling 

codecentric AG Use analysis tooling − Use the explain feature for search result scores ! − Use the validate/explain feature to see what the query looks like after rewriting ! − Make sure your analyzers work correctly − Use the analyze API or the „inquisitor“ plugin − And maybe the „extended-analyze“ plugin ! − When in doubt, take a look at the terms in your index − Use the Lucene tool „Luke“ − http://rosssimpson.com/blog/2014/05/06/using-luke-with-elasticsearch/ − The „skywalker“ plugin is also useful to understand Elasticsearch indexing 

codecentric AG Lessons learned Create regression tests 

codecentric AG Create regression tests − Set up a comprehensive test suite − Test expectations about matches − Prevent regressions when changing or modifying analyzers ! − The Elasticsearch Java client is embeddable − Consider using it for a unit test suite even if not using Java otherwise ! − Try it by solving the „mapping challenge“! − https://github.com/peschlowp/elasticsearch-mapping-challenge 

codecentric AG Lessons learned Know some Lucene basics 

codecentric AG Know some Lucene basics 

codecentric AG Know some Lucene basics Segment flush() 

codecentric AG Know some Lucene basics Segment flush() 

codecentric AG Know some Lucene basics Segment flush() Segment flush() 

codecentric AG Know some Lucene basics Segment flush() Segment flush() commit() Synced to Disk 

codecentric AG Know some Lucene basics Visible to newly opened readers Segment flush() Segment flush() commit() Synced to Disk If desired, visible via NRT 

codecentric AG Know some Lucene basics Segment flush() Segment flush() commit() Synced to Disk Executed heuristically (or explicitly via NRT) Explicit call (transaction) 

codecentric AG Know some Lucene basics 

codecentric AG Know some Lucene basics Persisted 

codecentric AG Know some Lucene basics Persisted refresh() 

codecentric AG Know some Lucene basics Persisted + Reopen reader
 for NRT refresh() Segment flush() 

codecentric AG Know some Lucene basics Persisted + Reopen reader
 for NRT refresh() Segment flush() 

codecentric AG Know some Lucene basics flush() Persisted + Reopen reader
 for NRT refresh() Segment flush() 

codecentric AG Know some Lucene basics Segment flush() Synced to Disk Persisted refresh() Segment flush() commit() + Reopen reader 

codecentric AG Know some Lucene basics Segment flush() Persisted refresh() Segment flush() Executed heuristically Executed regularly commit() Synced to Disk + Reopen reader 

codecentric AG Know some Lucene basics All documents persisted and searchable. Transaction log can be cleared. 

codecentric AG Lessons learned Understand updates 

codecentric AG Understand updates − With Lucene we have Update = Delete + Add ! − Elasticsearch offers two types of update: „Partial document“ and „Script“ − Internally still delete + add − This means even small updates might take a while ! − Partial document update trickiness − Updated fields are replaced, except for inner objects which are merged − To replace inner objects, consider wrapping them in a single-element array 

codecentric AG Lessons learned Understand relations 

codecentric AG Understand relations − Lucene documents are flat − Arrays of inner objects are flattened when indexing − Cannot build one-to-many relationships this way ! − Elasticsearch offers two alternatives: nested objects and parent/child mapping ! − Parent/child mapping is very flexible − Documents stay separated − May be queried together ! − Nested objects − Better performance, but documents are combined into one 

codecentric AG Lessons learned Embrace aliases 

codecentric AG Embrace aliases − A logical name for one or more Elasticsearch index(es) − Decouples client view from physical storage ! − Use cases − (Read-only) views on one or multiple indexes − Dynamically split indexes without affecting clients − Zero downtime reindexing ! − Limitations − Writes are only permitted for aliases that point to a single index ! − Recommendation: Use aliases right from the start 

codecentric AG Lessons learned Don’t underestimate reindexing 

codecentric AG Lessons learned Adjust default settings 

codecentric AG Adjust default settings − Choose a unique cluster name ! − Consider using unicast discovery ! − Configure (at least) these settings according to your requirements − minimum_master_nodes − gateway.recover_after_nodes − gateway.recover_after_time − gateway.expected_nodes 

codecentric AG Lessons learned Think about read preference 

codecentric AG Think about read preference − Search has a „preference“ parameter − Which shard (primary or some replica) to contact ! − By default „round robin“ − May cause some nasty effects − Documents marked as deleted still affect scoring − If segment merging on shards differs, may return different search results ! − Set „preference“ according to your needs: − Possible values: local, primary, only some shards or nodes, arbitrary string − One approach: Always direct a user to the same shard 

codecentric AG Lessons learned Satisfy scaling requirements 

codecentric AG Satisfy scaling requirements − Number of shards needs to be fixed at index creation time ! − Choose number of shards depending on estimation and measurements − A little overallocation is OK, yet shards don’t come for free ! − If it turns out you need to scale higher, index aliases may help − Create another, identically configured index and add new documents to it − Define an alias so that search considers both indexes − But: Requires additional effort for updates and deletes (which index to address?) − Alternative: Migrate to a new index with more shards ! − Remember: − Search 1 index with 50 shards =~ Search 50 indexes with 1 shard each − In both cases, 50 Lucene indexes are searched 

codecentric AG Lessons learned Give plugins a try 

codecentric AG Resources − The official blog
 http://www.elasticsearch.org/blog/ ! − The official book
 http://www.elasticsearch.org/guide/en/elasticsearch/guide/current/index.html ! − The official reference
 http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/index.html ! − The official Google Group
 https://groups.google.com/d/forum/elasticsearch ! − The Sense examples shown in this talk
 https://gist.github.com/peschlowp/560411af9bac3be909c0 

codecentric AG Questions? Dr. rer. nat. Patrick Peschlow
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