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And I'm back, as promised, with 5 more key differences meant to help you solve your Apache Solr vs Elasticsearch dilemma.
To help you properly evaluate the 2 open source search engines and, therefore, to identify the perfect fit for your own use case and your project's particular needs.
Another aspect that clearly differentiates the 2 search engines is the way(s) they handle node discovery.That is, whenever a new node joins the cluster or when there's something wrong with one of them, immediate measures, following certain criteria, need to be taken.
The 2 technologies handle this node-discovery challenge differently:
Machine learning has a way too powerful influence on the technological landscape these days not to take it into consideration in our Apache Solr vs Elasticsearch comparison here.
So, how do these 2 open source search engines support and leverage machine learning algorithms?
In any Apache Solr vs Elasticsearch comparison, the first one's richness in full-text search related features is just... striking!
Its codebase's simply “overcrowded” with text-focused features, such as:
Even so, Elasticsearch “strikes back” with its own dedicated suggesters API. And what this feature does precisely is hiding implementation details from user sight, so that we can add our suggestions far more easily.
And, we can't leave out its highlighting functionality (both search engines rely on Lucene for this), which is less configurable than in Apache Solr.
As already mentioned in this post, any Apache Solr vs Elasticsearch debate is a:
Text-search oriented approach vs Filtering and grouping analytical queries type of contrast.
Therefore, the 2 technologies are built, from the ground up, so that they approach different, specific use cases:
Moreover, each one comes with its own “toolbox” of tokenizers and analyzers for tackling text, for breaking it down into several terms/tokens to be indexed.
Speaking of which (indexing), I should also point out that the two search engine “giants” handle it differently:
Shard replacement: the last test that our two contestants here need to pass, so you can have your final answer to your “Apache Solr vs Elasticsearch” dilemma.
In this respect, Apache Solr is static, at least far more static than Elasticsearch. It calls for manual work for migrating shards whenever a Solr node joins or leaves the cluster.
Nothing impossible, simply less convenient and slightly more cumbersome for you:
Luckily for you, Elasticsearch is not just “more”, but “highly” dynamic and, therefore, far more independent.
It's capable to move around shards and indices, while you're being granted total control over shard placement:
The END! Now if you come to think about it, my 10-point comparative overview here could be summed up to 2 key ideas worth remembering:
We’re excited to hear your project.
Let’s collaborate!