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ClickStack vs OpenSearch

ClickHouse vs OpenSearch

ClickStack is a high-performance, open-source observability stack built on ClickHouse. It delivers high compression, lightning-fast queries and powerful aggregations across high cardinality logs, metrics, traces, session replays at petabyte scale.

OpenSearch, derived from Elasticsearch, remains anchored in a search-first architecture built on inverted indices. While effective for text search, this design falls short for modern observability workloads: it drives high disk usage, yields poor compression, and slows down queries at petabyte scale. Logs, metrics, and traces necessarily sit in separate indices, with no native way to join or analyze them together.

Why ClickStack is better than Lucene-based observability:

4x

Reduction in costs

10x

Faster analytical queries

2x

Better compression

Frustrated by slow queries, rising storage costs, and an endless need to scale horizontally? You’re not alone.

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Why ClickStack outperforms OpenSearch Observability

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Performance at scale

OpenSearch slows under heavy ingest and high-cardinality queries, while ClickHouse powers sub-second analytics even at petabyte scale.
Full data set aggregation for 1 billion JSON documents
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Lower cost, higher efficiency

ClickHouse’s columnar storage and advanced compression cut storage needs by > 50%, reducing infrastructure costs dramatically and allowing for long term retention.
Storage required for 1 billion JSON documents
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Unified observability

ClickStack runs logs, metrics, and traces in one engine alongside business and application data for unrivalled correlation. OpenSearch was never designed for analytical workloads leaving data fragmented.
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Operational simplicity

ClickStack eliminates the overhead of managing tens, hundreds, or even thousands of shards and the constant JVM tuning that comes with them. Its optimized engine scales vertically, handling massive datasets within a single shard - only requiring sharding at extreme volumes - reducing network overhead and costly rebalances.

ClickStack compared to OpenSearch for Observability

At a high level, OpenSearch and ClickStack share a familiar shape: both have a data collection layer (FluentBit and Data Prepper vs. OpenTelemetry), a storage engine (OpenSearch vs. ClickHouse), and a UI (OpenSearch dashboards vs. HyperDX). But beneath these parallels, the architectures diverge.
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How do the OpenSearch and ClickStack architectures differ?

As a fork of Elasticsearch, OpenSearch is a distributed search engine built around inverted indices and a shard-based architecture. While effective for full-text search, this design introduces high storage overhead, limited query parallelization, and heavy contention between ingest and query workloads.

ClickStack is powered by ClickHouse, a database built on a columnar, shared-nothing architecture optimized for analytics. It minimizes storage with advanced compression, parallelizes queries across all available cores, and separates storage from compute in the cloud to deliver fast, efficient observability at scale. Full SQL support unlocks deep data analysis.

ClickStack
  • Yes

    Native object storage support

  • Yes

    Aggregate states for accuracy

  • Yes

    Columnar storage with high compression

  • Yes

    ClickPipes for streaming ingest

  • Yes

    JSON with type fidelity

  • Yes

    1,000+ QPS per node

  • Yes

    Full parallelization within & across shards

  • Yes

    Full join support

  • Yes

    Materialized views (incremental & refreshable)

  • Yes

    Full SQL support

  • Yes

    Decoupled compute/storage (ClickHouse Cloud)

  • Yes

    Natural language search via HyperDX

  • Yes

    Self-managed & cloud

  • Yes

    Stateless compute nodes (ClickHouse Cloud)

  • Yes

    Real-time ingest supported

  • Yes

    Async inserts for small batches

  • Yes

    Inverted index support

  • Yes

    REST API support

  • Yes

    Incremental materialized views

OpenSearch
  • No

    Paid, Elastic Cloud Serverless only

  • No

    Terms aggregations are estimates

  • Intermediate

    Doc values provide columnar storage, not compressed

  • Intermediate

    Requires Data Prepper/third parties, no hosted ingestion

  • Intermediate

    First event field determines type

  • Intermediate

    Requires horizontal scaling/replicas

  • Intermediate

    Limited with accuracy implications; concurrent segment

  • Intermediate

    Limited Join support

  • Intermediate

    Limited incremental support; rollups and transforms typically rescan or aggregate large index segments per execution. Star-tree indexes precompute aggregates but support a narrow range of functions and filters.

  • Intermediate

    Limited syntax coverage

  • Intermediate

    AWS Serverless achieves partial decoupling via OCUs; not available in OSS

  • Yes

    Natural language search supported

  • Yes

    Self-managed & AWS OpenSearch

  • Yes

    AWS OpenSearch Serverless only

  • Yes

    Real-time ingest supported

  • Yes

    Inserts supported

  • Yes

    Inverted index supported

  • Yes

    REST API supported

  • Yes

    Ingest pipelines

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Lower costs

10x cost savings thanks to high compression and resource efficiency
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Simpler at scale

Homogenous architecture and vertical scaling simplifies and reduces nodes
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Built for high cardinality analytics

Column orientation designed for high cardinality queries
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Cloud-agnostic and open

Deploy on any cloud or on-premises. ClickStack’s open architecture avoids vendor lock-in and integrates seamlessly across ecosystems.

Migrate your workload from OpenSearch today

Cut costs, boost performance, and unlock observability at scale with ClickHouse.

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FAQs

We're here to make observability simple, fast, and open. Explore our FAQs to learn more about ClickStack, and if you don’t see what you need, we’re always happy to chat.

Ask us anything ->->

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