Apache Solr vs ElasticSearch

API

Feature	Solr 4.2	ElasticSearch 0.90.0.RC1
Format	XML,CSV,JSON	JSON
HTTP REST API
Binary API	SolrJ	TransportClient, Thrift (through a plugin)
JMX support
Client libraries	PHP, Ruby, Perl, Scala, Python, .NET, Javascript	PHP, Ruby, Perl, Scala, Python, .NET, Javascript, Erlang, Clojure
3rd-party product integration (open-source)	Drupal, Magento, Django, ColdFusion, Wordpress, OpenCMS, Plone, Typo3, ez Publish, Symfony2, Riak (via Yokozuna)	Django, Symfony2
3rd-party product integration (commercial)	DataStax Enterprise Search	SearchBlox

Indexing

Feature	Solr 4.2	ElasticSearch 0.90.0.RC1
Data Import	DataImportHandler - MySQL, CSV, XML, Tika, URL, Flat File	Rivers modules - Wikipedia, MongoDB, CouchDB, RabbitMQ, RSS, Sofa, JDBC, FileSystem, Dropbox, ActiveMQ, LDAP, Amazon SQS, St9, OAI, Twitter
ID field for updates and deduplication
Partial Doc Updates	with stored fields	with _source field
Custom Analyzers and Tokenizers
Per-field analyzer chain
Per-doc/query analyzer chain
Synonyms		Supports Solr and Wordnet synonym format
Multiple indexes
Near-Realtime Search/Indexing
Complex documents	Flat document structure. No native support for nesting documents
Multiple document types per schema	One set of fields per schema, one schema per core
Online schema changes	Schema change requires restart. Workaround possible using MultiCore.	Only backward-compatible changes.
Apache Tika integration
Dynamic fields
Field copying		via multi-fields
Hash-based deduplication

Searching

Feature	Solr 4.2	ElasticSearch 0.90.0.RC1
Lucene Query parsing
Structured Query DSL	Need to programmatically create queries if going beyond Lucene query syntax.
Span queries	via SOLR-2703
Spatial search
Multi-point spatial search
Faceting		The way top N facets work now is by getting the top N from each shard, and merging the results. This can give incorrect counts when num shards > 1.
Pivot Facets
More Like This
Boosting by functions
Boosting using scripting languages
Push Queries		Percolation
Field collapsing/Results grouping		possibly 0.20+
Spellcheck		Suggest API
Autocomplete		Beta implementation from community plugin
Query elevation
Joins	It's not supported in distributed search. See LUCENE-3759.	via has_children and top_children queries
Filter queries		also supports filtering by native scripts
Filter execution order	local params and cache property	_cache and _cache_key property
Alternative QueryParsers	DisMax, eDisMax	query_string, dis_max, match, multi_match etc
Negative boosting	but awkward. Involves positively boosting the inverse set of negatively-boosted documents.
Search across multiple indexes	it can search across multiple compatible collections
Result highlighting
Custom Similarity
Searcher warming on index reload		Warmers API

Customizability

Feature	Solr 4.2	ElasticSearch 0.90.0.RC1
Pluggable API endpoints
Pluggable search workflow	via SearchComponents
Pluggable update workflow
Pluggable Analyzers/Tokenizers
Pluggable Field Types
Pluggable Function queries
Pluggable scoring scripts
Pluggable hashing

Distributed

Feature	Solr 4.2	ElasticSearch 0.90.0.RC1
Self-contained cluster	Depends on separate ZooKeeper server	Only ElasticSearch nodes
Automatic node discovery	ZooKeeper	internal Zen Discovery or ZooKeeper
Partition tolerance	The partition without a ZooKeeper quorum will stop accepting indexing requests or cluster state changes, while the partition with a quorum continues to function.	Partitioned clusters can diverge unless discovery.zen.minimum_master_nodes set to at least N/2+1, where N is the size of the cluster. If configured correctly, the partition without a quorum will stop operating, while the other continues to work. See this
Automatic failover	If all nodes storing a shard and its replicas fail, client requests will fail, unless requests are made with the shards.tolerant=true parameter, in which case partial results are retuned from the available shards.
Automatic leader election
Shard replication
Sharding
Automatic shard rebalancing		it can be machine, rack, availability zone, and/or data center aware. Arbitrary tags can be assigned to nodes and it can be configured to not assign the same shard and its replicates on a node with the same tags.
Change # of shards	specified at index-creation time, with command-line param -DnumShards=n. Cannot be changed once index is created. Shard splitting is a work in progress (SOLR-3755). Additional replicas can be created.	each index has 5 shards by default. Number of primary shards cannot be changed once the index is created. Replicas can be increased anytime.
Relocate shards and replicas		can move shards and replicas to any node in the cluster on demand
Control shard routing	with some config changes	routing parameter
Consistency	Indexing requests are synchronous with replication. A indexing request won't return until all replicas respond. No check for downed replicas. They will catch up when they recover. When new replicas are added, they won't start accepting and responding to requests until they are finished replicating the index.	Replication between nodes is synchronous by default, thus ES is consistent by default, but it can be set to asynchronous on a per document indexing basis. Index writes can be configured to fail is there are not sufficient active shard replicas. The default is quorum, but all or one are also available.

Thoughts...

As a number of folks point out in the discussion below, feature comparisons are inherently shallow and only go so far. I think they serve a purpose, but shouldn't be taken to be the last word on these 2 fantastic search products.

If you're running a smallish site and need search features without the distributed bells-and-whistles, I think you'll be very happy with either Solr or ElasticSearch.

The exception to this is if you need RIGHT NOW some very specific feature like field grouping which is currently implemented in Solr and not ElasticSearch. Because of the considerable momentum behind ElasticSearch, it is very likely that the feature-set between the 2 products will converge considerably in the near future.

If you're planning a large installation that requires running distributed search instances, I suspect you're going to be happier with ElasticSearch.

As Matt Weber points out below, ElasticSearch was built to be distributed from the ground up, not tacked on as an 'afterthought' like it was with Solr. This is totally evident when examining the design and architecture of the 2 products, and also when browsing the source code.