One of the common questions among professionals dealing with huge amount of data is usually about which search platform to use. Is it Solr or Elasticsearch that suits your requirements? Don’t be confused anymore. Check out how to scale the better and easier option by analysing the significant differences between Solr and Elasticsearch search engines.
Solr vs Elasticsearch: The Main Differences
- Performance and Scalability: Solr and Elasticsearch are almost equal in terms of performance. However, Solr is good when dealing with static data and offers full precision for fast data analysis, while Elasticsearch may lose precision because of the way in which data in the shards is placed.
- Age and Maturity: Solr was first released in 2006 and considered as the most common search engine among developers. Along with indexing and searching, it offers some rich functionalities such as faceting, grouping, excellent filtering, pluggable document processing, etc. Whereas, Elasticsearch was introduced in 2010 as another search option with considerably fair features that eased the handling of large indices and high query rates.
- Open source: Elasticsearch is widely used by organizations for open source log management. Though, both the platforms are released under the Apache Software License, yet Solr is considered to be truly open-source. When any user who wants to add a missing functionality or code to Solr or Elasticsearch, it is easier in case of Solr. Both the software have lively developer communities which are rapidly being developed.
- Learning and Support: To get started, Elasticsearch may seem to be easier than Solr. Elasticsearch allows users to begin their journey with just a single download and a command. Contrastingly, Solr requires more knowledge and skills to start with it.
- Configuration: Solr requires well managed-schema file (xml file) to define the indexing structure, fields and their types. But, Elasticsearch is schemaless as it does not require creation of any such index xml file to start.
- Node Discovery:Another major difference between Elasticsearch and Solr is the node discovery and cluster management in general. The main purpose of discovery is to monitor nodes’ states, choose master nodes, and in some cases also store shared configuration files.
- Shard Placement: Elasticsearch is quite dynamic when it comes to placement of its indices and shards. Whenever an action takes place, it easily moves shards around the cluster. It also allows users control the placement of shard using awareness tags and make Elasticsearch move shards on demand with an API call. On the other hand, Solr is more static type. It does not do anything on its own when a Solr node joins or leaves the cluster.
- Analytics Engine: Solr offers many data analysis capabilities such as the old facets, JSON facets, streaming expressions etc. Elasticsearch provides a powerful search engine that can perform one level data analysis as well as nest data analysis. It also offers support for matrix aggregation allowing computation of statistics over a set of fields.
- Caches: The architecture of Elasticsearch and Solr are quite different. Solr has global cashes i.e. it enables a single cache instance of a given type for a shard in all segments. Therefore, when a single segment modifies, it invalidates and refreshes the whole cache, thereby consuming extra time and hardware resources too. Whereas, Elasticsearch has caches for every segment. Thus, when a single segment changes, only a portion of the cache is invalidated and refreshed.
Though both are open source platforms and built on the core underlying search library, Lucene, yet both are different in a number of ways such as scalability, manageability, ease of deployment, community presence etc. In brief, Solr is preferred for static data whereas Elasticsearch is better suited for timeseries data use cases.