# Elasticsearch vs Solr > Elasticsearch and Solr both build distributed search on Apache Lucene, so choose based on operational fit, query requirements, and licensing rather than assuming one is faster. Pick Elasticsearch when its document APIs and surrounding tooling fit your stack; pick Solr when its search configuration model, SolrCloud architecture, or Apache licensing better fits your requirements. Source: https://sysround.com/compare/elasticsearch-vs-solr Updated: 2026-11-05 ## Side by side | | Elasticsearch | Solr | |---|---|---| | Search foundation | Lucene indexes; full-text search, filters, aggregations | Lucene indexes; full-text search, filters, facets | | Data model | JSON documents with explicit or dynamic mappings | Documents with managed schemas and dynamic fields | | Query interface | JSON Query DSL and aggregation APIs | Query parsers, request handlers, JSON Request and Facet APIs | | Cluster coordination | Built-in coordination using master-eligible nodes | SolrCloud uses ZooKeeper for coordination | | Partitioning | Indexes divided into primary and replica shards | Collections divided into shards with leader and replica roles | | Search freshness | Refresh makes indexed changes searchable | Soft commits open searchers to expose changes | | Operational fit | Elasticsearch APIs and Elastic tooling | Solr configuration sets, handlers, and ZooKeeper operations | | Licensing | Terms depend on distribution, source option, and features | Apache License 2.0 | ## The differences that matter The shared Lucene foundation matters more than a feature checklist. Both support analyzers, relevance scoring, filtering, faceting, and distributed retrieval. Query latency depends on document shape, shard fan-out, filter selectivity, cache behavior, and hardware. Test representative queries rather than claiming either engine is universally faster. The application-facing configuration differs. Elasticsearch centers on index mappings and a JSON Query DSL; Solr exposes schemas, configuration sets, request handlers, and multiple query parsers. Both allow explicit schemas and dynamic field handling. For user-generated metadata, constrain field creation in either engine so arbitrary keys do not produce uncontrolled schema growth. The cluster dependency model changes operations. Elasticsearch manages cluster coordination through master-eligible nodes. SolrCloud depends on ZooKeeper, which needs its own quorum, monitoring, and recovery plan. Both still require deliberate shard placement, replica capacity, failure testing, and backups; replication is not a substitute for a recoverable snapshot. Neither engine makes an acknowledged write immediately visible to every search. Elasticsearch refreshes and Solr soft commits control search visibility, while durability has separate mechanisms and settings. In either design, define an indexing-lag target and explain what happens when ingestion falls behind. For private content, do not rely solely on asynchronously indexed permissions. Licensing can eliminate an otherwise suitable option. Solr uses Apache License 2.0. Elasticsearch licensing depends on the distribution, source licensing option, and features involved, so verify the exact artifact and intended use, especially when embedding or offering a hosted service. Do not describe Elasticsearch and Solr as having equivalent licensing. ## Choose Elasticsearch when - Your team already operates Elasticsearch and has tested templates, dashboards, backup procedures, and incident runbooks. - Your application fits Elasticsearch's document APIs, Query DSL, and aggregation model without substantial adaptation. - You want integrations in the Elastic ecosystem and have verified the availability and licensing of the features you need. - You prefer built-in cluster coordination over operating the ZooKeeper dependency required by SolrCloud. ## Choose Solr when - Apache License 2.0 is an explicit requirement for the search component you distribute or operate. - Your team already runs SolrCloud and can reuse proven schemas, configuration sets, request handlers, and ZooKeeper operations. - Your search application benefits from Solr's query parsers and configurable request handlers, and the team understands how to tune them. - An existing Solr deployment meets your relevance, freshness, and capacity targets, with no measured benefit that justifies migration. ## Worked example: Marketplace search with a two-second freshness target Suppose a marketplace has 50 million listings, 2,000 searches per second, and 500 listing updates per second, with a two-second search freshness target. Either engine is a candidate; those numbers alone do not determine a winner or a shard count. Feed database changes into the index, then test keyword queries combined with category, price, and location filters. Measure tail latency and indexing lag during a node failure. If the team already operates SolrCloud reliably, keep that advantage unless tests expose a gap. If it already uses Elasticsearch tooling, that may favor Elasticsearch. In both cases, recheck current listing availability before purchase rather than trusting stale search results. ## In the interview - Start with requirements: searchable fields, filters, relevance, query volume, update volume, freshness, and access control. Name the engine after explaining the search architecture. - Describe the source-of-truth database feeding a rebuildable search index through change capture or an outbox. Expect questions about duplicate events, ordering, deletions, and replay. - Explain shard routing and fan-out. More shards can increase parallelism but also add coordination and resource overhead; replicas can add read capacity but do not remove every bottleneck. - Separate write durability, search visibility, and authorization correctness. Expect a follow-up about a deleted or newly private document that remains in a stale index. - Justify operational fit without unsupported speed claims. Propose a workload test covering tail latency, indexing lag, relevance, and behavior during node failure. ## Common mistakes - Calling Elasticsearch schemaless or Solr rigidly schema-bound. Both support explicit field definitions and dynamic field handling. - Claiming Elasticsearch is for analytics while Solr is only for text search. Both support full-text retrieval and distributed aggregations or facets. - Treating an acknowledged indexing request as proof that a document is searchable, or confusing visibility settings with durability settings. - Choosing by brand without discussing shard fan-out, index rebuilds, permission changes, operating experience, or licensing. ## Interview questions where it comes up - [Design Facebook post search](https://sysround.com/questions/design-facebook-post-search): Either engine can implement distributed keyword retrieval, but a strong answer must also handle indexing lag and permission changes. - [Design Facebook Marketplace](https://sysround.com/questions/design-facebook-marketplace): Listing discovery combines text, location, and structured filters while requiring current listing state to be checked outside stale search results. - [Design a restaurant review app like Yelp](https://sysround.com/questions/design-yelp): Restaurant discovery needs ranked text and geographic search, plus reliable propagation of review edits and deletions. - [Design Wikipedia or Notion](https://sysround.com/questions/design-wikipedia): Document search is a derived index that must track version changes and enforce access rules even when indexing is delayed. ## FAQ ### Is Elasticsearch faster than Solr? There is no universal winner. Both use Lucene, and performance depends on analyzers, mappings or schemas, shard layout, query shape, caching, and hardware. Compare representative workloads at equivalent durability, replication, and freshness settings. ### What is the main difference between Elasticsearch and Solr? The main differences are their APIs, configuration models, cluster coordination, ecosystems, and licensing. Elasticsearch uses built-in cluster coordination, while SolrCloud uses ZooKeeper. Their shared Lucene foundation means many core search capabilities overlap. ### Should I choose Elasticsearch or Solr in a system design interview? Either is defensible when it fits the requirements. Explain the inverted index, ingestion pipeline, partitioning, freshness target, and permission enforcement before choosing a product. Then justify the choice through operational experience, query needs, and licensing constraints. ## Related concepts - [Full-text search](https://sysround.com/concepts/full-text-search) - [Inverted index](https://sysround.com/concepts/inverted-index) - [Database sharding](https://sysround.com/concepts/database-sharding) - [Eventual consistency](https://sysround.com/concepts/eventual-consistency) - [Change data capture](https://sysround.com/concepts/change-data-capture) --- SysRound (https://sysround.com) is an AI practice tool for system design interviews: a voice or typed AI interviewer, a shared whiteboard, then feedback on every skill and a model answer. Its feedback is AI-generated and can be wrong.