Solr vs ElasticSearch

Question

What are the core architectural differences between these technologies     Also  what use cases are generally more appropriate for each

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I have use Elasticsearch for 3 years and Solr for about a month  I feel elasticsearch cluster is quite easy to install as compared to Solr installation  Elasticsearch has a pool of help documents with great explanation  One of the use case I was stuck up with Histogram Aggregation which was available in ES however not found in Solr

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While all of the above links have merit  and have benefited me greatly in the past  as a linguist  exposed  to various Lucene search engines for the last 15 years  I have to say that elastic-search development is very fast in Python  That being said  some of the code felt non-intuitive to me  So  I reached out to one component of the ELK stack  Kibana  from an open source perspective  and found that I could generate the somewhat cryptic code of elasticsearch very easily in Kibana  Also  I could pull Chrome Sense es queries into Kibana as well  If you use Kibana to evaluate es  it will further speed up your evaluation  What took hours to run on other platforms was up and running in JSON in Sense on top of elasticsearch  RESTful interface  in a few minutes at worst  largest data sets   in seconds at best  The documentation for elasticsearch  while 700  pages  didn t answer questions I had that normally would be resolved in SOLR or other Lucene documentation  which obviously took more time to analyze  Also  you may want to take a look at Aggregates in elastic-search  which have taken Faceting to a new level    Bigger picture  if you re doing data science  text analytics  or computational linguistics  elasticsearch has some ranking algorithms that seem to innovate well in the information retrieval area  If you re using any TF IDF algorithms  Text Frequency Inverse Document Frequency  elasticsearch extends this 1960 s algorithm to a new level  even using BM25  Best Match 25  and other Relevancy Ranking algorithms  So  if you are scoring or ranking words  phrases or sentences  elasticsearch does this scoring on the fly  without the large overhead of other data analytics approaches that take hours--another elasticsearch time savings   With es  combining some of the strengths of bucketing from aggregations with the real-time JSON data relevancy scoring and ranking  you could find a winning combination  depending on either your agile  stories  or architectural use cases  approach   Note  did see a similar discussion on aggregations above  but not on aggregations and relevancy scoring--my apology for any overlap  Disclosure  I don t work for elastic and won t be able to benefit in the near future from their excellent work due to a different architecural path  unless I do some charity work with elasticsearch  which wouldn t be a bad idea

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Update  Now that the question scope has been corrected  I might add something in this regard as well   There are many comparisons between Apache Solr and ElasticSearch available  so I ll reference those I found most useful myself  i e  covering the most important aspects    Bob Yoplait already linked kimchy s answer to ElasticSearch  Sphinx  Lucene  Solr  Xapian  Which fits for which usage   which summarizes the reasons why he went ahead and created ElasticSearch  which in his opinion provides a much superior distributed model and ease of use in comparison to Solr  Ryan Sonnek s Realtime Search  Solr vs Elasticsearch provides an insightful analysis comparison and explains why he switched from Solr to ElasticSeach  despite being a happy Solr user already - he summarizes this as follows          Solr may be the weapon of choice when building standard search   applications  but Elasticsearch takes it to the next level with an   architecture for creating modern realtime search applications    Percolation is an exciting and innovative feature that singlehandedly   blows Solr right out of the water  Elasticsearch is scalable  speedy   and a dream to integrate with  Adios Solr  it was nice knowing you   emphasis mine   The Wikipedia article on ElasticSearch quotes a comparison from the reputed German iX magazine  listing advantages and disadvantages  which pretty much summarize what has been said above already          Advantages          ElasticSearch is distributed  No separate project required  Replicas are near real-time too  which is called  Push replication     ElasticSearch fully supports the near real-time search of Apache   Lucene    Handling multitenancy is not a special configuration  where   with Solr a more advanced setup is necessary    ElasticSearch introduces   the concept of the Gateway  which makes full backups easier          Disadvantages          Only one main developer  not applicable anymore according to the current elasticsearch GitHub organization  besides having a pretty active committer base in the first place    No autowarming feature  not applicable anymore according to the new Index Warmup API          Initial Answer  They are completely different technologies addressing completely different use cases  thus cannot be compared at all in any meaningful way    Apache Solr - Apache Solr offers Lucene s capabilities in an easy to use  fast search server with additional features like faceting  scalability and much more Amazon ElastiCache - Amazon ElastiCache is a web service that makes it easy to deploy  operate  and scale an in-memory cache in the cloud    Please note that Amazon ElastiCache is protocol-compliant with Memcached  a widely adopted memory object caching system  so code  applications  and popular tools that you use today with existing Memcached environments will work seamlessly with the service  see Memcached for details       emphasis mine   Maybe this has been confused with the following two related technologies one way or another    ElasticSearch - It is an Open Source  Apache 2   Distributed  RESTful  Search Engine built on top of Apache Lucene  Amazon CloudSearch - Amazon CloudSearch is a fully-managed search service in the cloud that allows customers to easily integrate fast and highly scalable search functionality into their applications    The Solr and ElasticSearch offerings sound strikingly similar at first sight  and both use the same backend search engine  namely Apache Lucene   While Solr is older  quite versatile and mature and widely used accordingly  ElasticSearch has been developed specifically to address Solr shortcomings with scalability requirements in modern cloud environments  which are hard er  to address with Solr   As such it would probably be most useful to compare ElasticSearch with the recently introduced Amazon CloudSearch  see the introductory post Start Searching in One Hour for Less Than  100   Month   because both claim to cover the same use cases in principle

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I see some of the above answers are now a bit out of date   From my perspective  and I work with both Solr Cloud and non-Cloud  and ElasticSearch on a daily basis  here are some interesting differences    Community  Solr has a bigger  more mature user  dev  and contributor community   ES has a smaller  but active community of users and a growing community of contributors Maturity  Solr is more mature  but ES has grown rapidly and I consider it stable Performance  hard to judge   I we have not done direct performance benchmarks   A person at LinkedIn did compare Solr vs  ES vs  Sensei once  but the initial results should be ignored because they used non-expert setup for both Solr and ES  Design  People love Solr   The Java API is somewhat verbose  but people like how it s put together   Solr code is unfortunately not always very pretty   Also  ES has sharding  real-time replication  document and routing built-in   While some of this exists in Solr  too  it feels a bit like an after-thought  Support  there are companies providing tech and consulting support for both Solr and ElasticSearch   I think the only company that provides support for both is Sematext  disclosure  I m Sematext founder  Scalability  both can be scaled to very large clusters   ES is easier to scale than pre-Solr 4 0 version of Solr  but with Solr 4 0 that s no longer the case    For more thorough coverage of Solr vs  ElasticSearch topic have a look at https   sematext com blog solr-vs-elasticsearch-part-1-overview    This is the first post in the series of posts from Sematext doing direct and neutral Solr vs  ElasticSearch comparison  Disclosure  I work at Sematext

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I see that a lot of folks here have answered this ElasticSearch vs Solr question in terms of features and functionality but I don t see much discussion here  or elsewhere  regarding how they compare in terms of performance   That is why I decided to conduct my own investigation  I took an already coded heterogenous data source micro-service that already used Solr for term search  I switched out Solr for ElasticSearch then I ran both versions on AWS with an already coded load test application and captured the performance metrics for subsequent analysis   Here is what I found  ElasticSearch had 13  higher throughput when it came to indexing documents but Solr was ten times faster  When it came to querying for documents  Solr had five times more throughput and was five times faster than ElasticSearch

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Add an nested document in solr very complex and nested data search also very complex  but Elastic Search easy to add nested document and search

User · Answer

If you are already using SOLR  remain stick to it  If you are starting up  go for Elastic search   Maximum major issues have been fixed in SOLR and it is quite mature

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I only use Elastic-search  Since I found solr is very hard to start  Elastic-search s features    Easy to start  very few setting  Even a newbie can setup a cluster step by step  Simple Restful API which using NoSQL query  And many language libraries for easy accessing  Good document  you can read the book    There is a web version on official website

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I have created a table of major differences between elasticsearch and Solr and splunk  you can use it as 2016 update

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Imagine the use case    A lot 100   of small 10Mb-100Mb  1000-100000 documents  search indexes  They are using by a lot of applications  microservices  Each application can use more than one index Small by size index  yes  But huge load hundreds search-requests per second  and requests are complex  multiple aggregations  conditions and so on  Downtimes are not allowed All of that is working years long  and constantly growing    Idea to have individual ES instance per each index - is huge overhead in this case   Based on my experience  this kind of use case is very complex to support with Elasticsearch   Why   FIRST   The major problem is fundamental back compatibility disregard   Breaking changes are so cool    Note  imagine SQL-server which require you to do small change in all your SQL-statements  when upgraded    can t imagine it  But for ES it s normal   Deprecations which will dropped in next major release are so sexy    Note  you know  Java contain some deprecations  which 20  years old  but still working in actual Java version       And not only that  sometimes you even have something which nowhere documented  personally came across only once but       So  If you want to upgrade ES  because you need new features for some app or you want to get bug fixes  - you are in hell  Especially if it is about major version upgrade   Client API will not back compatible  Index settings will not back compatible  And upgrade all app services same moment with ES upgrade is not realistic   But you must do it time to time  No other way   Existing indexes is automatically upgraded  - Yes  But it not help you when you will need to change some old-index settings   To live with that  you need constantly invest a lot of power in     forward compatibility of you apps services with future releases of ES  Or you need to build and anyway constantly support  some kind of middleware between you app services and ES  which provide you back compatible client API   And  you can t use Transport Client  because it required jar upgrade for every minor version ES upgrade   and this fact do not make your life easier   Is it looks simple  amp  cheap  No  it s not  Far from it  Continuous maintenance of complex infrastructure which based on ES  is way to expensive in all possible senses   SECOND  Simple API   Well    no really   When you is really using complex conditions and aggregations     JSON-request with 5 nested levels is whatever  but not simple     Unfortunately  I have no experience with SOLR  can t say anything about it   But Sphinxsearch is much better it this scenario  becasue of totally back compatible SphinxQL    Note   Sphinxsearch Manticore are indeed interesting  It s not Lucine based  and as result seriously different  Contain several unique features from the box which ES do not have and crazy fast with small middle size indexes

User · Answer

Since the long history of Apache Solr  I think one strength of the Solr is its ecosystem  There are many Solr plugins for different types of data and purposes      Search platform in the following layers from bottom to top    Data   Purpose  Represent various data types and sources  Document building   Purpose  Build document information for indexing  Indexing and searching   Purpose  Build and query a document index  Logic enhancement   Purpose  Additional logic for processing search queries and results  Search platform service   Purpose  Add additional functionalities of search engine core to provide a service platform   UI application   Purpose  End-user search interface or applications    Reference article   Enterprise search

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I have been working on both solr and elastic search for  Net applications  The major difference what i have faced is  Elastic search      More code and less configuration  however there are api s to change but still is a code change for complex types  type within types i e nested types wasn t able to achieve in solr    Solr     less code and more configuration and hence less maintenance for grouping results during querying lots of work to achieve in elastic    search in short no straight way

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