What is OpenSearch and how does it fit into a modern data platform?

OpenSearch is an open-source search and analytics engine built on Apache Lucene. It lets you index, search, and visualize large volumes of structured and unstructured data in near real time. Amazon Web Services forked it from Elasticsearch 7.10 in 2021, and it’s now maintained as a community-driven project under the OpenSearch Software Foundation. What follows covers how it works, where it fits in a modern data stack, and when it makes sense to pick it over other analytics tools.

What problems does OpenSearch solve in a data stack?

The core problem OpenSearch solves is making large, fast-moving datasets searchable and observable without needing a specialized query language or a dedicated data science team. It handles full-text search, log aggregation, and real-time analytics in one system, so teams can query event data, application logs, and metrics through a single interface rather than routing different data types to different tools.

In practice, the problems it addresses fall into a few distinct categories:

Log and event observability: Collecting logs from distributed systems and making them searchable within seconds of ingestion is one of OpenSearch’s core strengths. Operations teams can correlate events across services without waiting for batch processing cycles.
Full-text search over operational data: When your data contains free-form text, such as product descriptions, support tickets, and document archives, relational databases and columnar stores are poor fits. OpenSearch handles relevance ranking, fuzzy matching, and faceted filtering natively.
Real-time dashboards: OpenSearch Dashboards, the built-in visualization layer, lets you build live operational views without exporting data to a separate BI tool.
Alerting and anomaly detection: The platform includes plugins for threshold-based alerting and machine-learning-based anomaly detection, which cuts down on the need for external monitoring infrastructure.

What OpenSearch doesn’t handle well is complex analytical SQL queries over historical data at petabyte scale. For that, a dedicated query engine like Trino is a better fit, and the two tools are often used together in the same platform.

How does OpenSearch work under the hood?

OpenSearch ingests documents, converts them into an inverted index using Apache Lucene, and distributes that index across a cluster of nodes. When you send a query, it fans out across relevant shards in parallel, collects partial results, and merges them into a ranked response. That architecture is what gives OpenSearch its low-latency search characteristics even at high data volumes.

The key components of an OpenSearch cluster are:

Nodes: Individual processes that each hold a subset of the data. Nodes are assigned roles: cluster manager, data, ingest, and coordinating.
Shards: Each index is split into primary shards, which distribute data across nodes. Replica shards provide redundancy and increase read throughput.
Indices: Logical groupings of documents. Think of an index roughly like a database table, though the schema is flexible and changes don’t require migrations in the traditional sense.
Segments: Lucene writes data in immutable segments that are periodically merged. This is why OpenSearch is optimized for append-heavy workloads rather than frequent updates.

Ingestion typically goes through an ingest pipeline, where you can apply processors to parse, enrich, or transform documents before they’re indexed. For high-throughput scenarios, Apache Kafka® is commonly placed upstream to buffer and deliver data reliably into the cluster.

What’s the difference between OpenSearch and Elasticsearch?

OpenSearch and Elasticsearch share a common codebase up to version 7.10, but they’ve diverged significantly since the 2021 fork. The core architectural concepts are similar, and most query syntax is compatible, but the two projects now have different governance models, licensing terms, and feature roadmaps. The most important practical distinction is licensing: OpenSearch runs under Apache 2.0, while Elasticsearch moved to a dual-license model that restricts certain commercial use cases.

Governance and licensing

OpenSearch is governed by the OpenSearch Software Foundation, an independent body under the Linux Foundation. No single vendor controls the roadmap, and the Apache 2.0 license gives you full freedom to run, modify, and distribute the software without restriction. Elasticsearch is developed and commercially controlled by Elastic NV, and its current licensing terms have been a point of contention in the open-source community.

Feature divergence

Both projects have added features the other doesn’t have. OpenSearch has invested in security as a built-in, free capability, including fine-grained access control, audit logging, and encryption at rest, features that in Elasticsearch require a paid subscription tier. Elasticsearch has focused on relevance improvements and its own vector search capabilities. For k-nearest-neighbor vector search, both now support it, but the implementations differ. Migrating between the two is possible but requires testing, particularly around index compatibility and plugin equivalence.

What are the main use cases for OpenSearch?

The main use cases are log analytics and observability, full-text search over application data, security information and event management (SIEM), and e-commerce or content search. Each benefits from OpenSearch’s combination of fast ingestion, flexible schema, and near-real-time query response.

Log analytics and observability: Aggregating logs from microservices, infrastructure components, and applications into a central, searchable store. OpenSearch’s index lifecycle management lets you automatically move older data to cheaper storage tiers.
SIEM and threat detection: Security teams use OpenSearch to correlate events across systems, apply detection rules, and trigger alerts. The built-in anomaly detection plugin supports unsupervised learning over time-series data.
Application search: Product catalogs, document libraries, and knowledge bases benefit from OpenSearch’s relevance tuning, synonym support, and multi-language analysis.
Operational dashboards: Real-time views of business metrics, infrastructure health, or pipeline status, built directly in OpenSearch Dashboards without exporting data elsewhere.

One area where OpenSearch is increasingly used is vector search for semantic retrieval, which supports retrieval-augmented generation (RAG) patterns in AI applications. This is a relatively recent addition to the project and worth evaluating carefully against your latency and scale requirements before committing.

How does OpenSearch integrate with Apache Kafka and other data platform tools?

OpenSearch integrates with Apache Kafka® through the OpenSearch Kafka Connector, a Kafka Connect sink connector that reads records from Kafka topics and writes them to OpenSearch indices. This is one of the most common patterns in modern data platforms: Kafka handles durable, high-throughput event streaming, and OpenSearch handles indexing and search. The connector manages batching, retries, and schema mapping, so you don’t need custom ingestion code for standard workloads.

Beyond Kafka, OpenSearch fits into a broader open-source data stack in several ways:

Apache Spark™: Spark can write processed data to OpenSearch using the opensearch-hadoop connector. This is useful when you want to run batch transformations in Spark and make the results searchable.
Apache Airflow: Airflow can orchestrate index management tasks, such as rolling over indices, triggering reindexing jobs, or running data quality checks before ingestion.
Trino: Some teams connect Trino to OpenSearch using the Elasticsearch connector, which is compatible with OpenSearch. This allows SQL queries to span OpenSearch indices alongside other data sources, though performance characteristics differ from native OpenSearch queries.
Fluent Bit and Logstash: These log shippers are the standard way to route log data from applications and infrastructure into an OpenSearch cluster.

Integration is generally straightforward because OpenSearch exposes a REST API that most data tools already know how to speak. The main engineering effort is usually around schema design and index lifecycle management, not connectivity.

How does OpenSearch run on Kubernetes?

OpenSearch runs on Kubernetes through a dedicated operator that manages the full lifecycle of an OpenSearch cluster as a set of Kubernetes custom resources. You define your desired cluster topology in a manifest, and the operator handles provisioning nodes, configuring roles, managing certificates, and rolling upgrades. That makes cluster management reproducible and auditable, which matters in regulated environments.

Running OpenSearch on Kubernetes does require attention to a few platform-specific concerns:

Persistent storage: OpenSearch is stateful. Each data node needs a persistent volume claim (PVC) with appropriate storage class and IOPS characteristics. On-premises deployments often use local SSDs with a storage class that pins volumes to specific nodes.
Memory configuration: OpenSearch relies heavily on the JVM heap and the OS page cache. The JVM heap should be set to no more than half the available node memory, and Kubernetes memory limits must account for both. Misconfiguring this is a common source of out-of-memory kills.
Node roles and pod topology: Separating cluster manager nodes from data nodes into different Kubernetes deployments gives you independent scaling and reduces the risk of a data node restart affecting cluster manager quorum.
Security context: OpenSearch requires the vm.max_map_count kernel parameter to be set to at least 262144. On Kubernetes, this typically requires a privileged init container or a node-level configuration through a DaemonSet.

If you’re looking at hands-on platform examples, running stateful workloads like OpenSearch on Kubernetes is a representative case of the operational complexity that operators are designed to reduce.

When should you choose OpenSearch over a dedicated analytics database?

Choose OpenSearch when your primary requirement is fast full-text search, log exploration, or real-time operational visibility, and when your query patterns are document-oriented rather than relational. If you’re running ad hoc SQL queries over structured historical data, aggregating large fact tables, or building a data warehouse, a columnar query engine like Trino or a dedicated OLAP store is a better fit.

The decision usually comes down to a few concrete criteria:

Query type: Free-text search, fuzzy matching, and relevance ranking favor OpenSearch. Complex joins, window functions, and multi-table aggregations favor a SQL engine.
Data freshness requirement: If you need data queryable within seconds of ingestion, OpenSearch is well suited. Columnar databases optimized for batch analytics may have higher ingestion latency.
Schema flexibility: OpenSearch handles semi-structured and schema-on-write data more naturally than strict columnar stores. If your log or event schema changes frequently, OpenSearch’s dynamic mapping reduces the operational overhead.
Operational cost: OpenSearch clusters are memory-intensive. If your use case is primarily structured analytics with a stable schema, a columnar store will typically deliver better cost efficiency per query.

In many production data platforms, OpenSearch and a dedicated analytics engine coexist. OpenSearch handles the operational and observability layer, while Trino or a similar engine handles the analytical layer. The two are complementary rather than competing.

How Stackable helps with OpenSearch on Kubernetes

The Stackable Data Platform (SDP) includes a dedicated OpenSearch Operator that manages OpenSearch clusters on Kubernetes as first-class citizens. Rather than maintaining custom Helm charts or hand-written manifests, you declare the desired state of your OpenSearch cluster in a Kubernetes custom resource, and the operator handles the rest: node provisioning, certificate management, configuration updates, and rolling upgrades.

Concretely, the SDP gives you:

Integrated operator lifecycle: The OpenSearch Operator is versioned and tested alongside the other operators in the SDP, so upgrades across the stack stay coordinated and predictable.
Security by default: TLS and authentication are configured out of the box, without manual certificate generation or plugin configuration.
Composability with Kafka: Because the Stackable Operator for Apache Kafka® runs in the same platform, wiring Kafka to OpenSearch follows a consistent configuration model rather than requiring separate tooling for each integration.
Infrastructure as code: All cluster definitions are declarative YAML, which means your OpenSearch setup is reproducible, version-controlled, and auditable, a requirement in many regulated industries.
Cloud-agnostic deployment: The SDP runs on-premises, in any cloud, or in hybrid environments, so your OpenSearch cluster isn’t tied to a specific provider’s managed service.

If you want to see what a working OpenSearch setup looks like inside the SDP, the demo environment is a good starting point. For questions about how OpenSearch fits your specific architecture, the Stackable team is available to talk through your setup directly.