{"slug":"best-real-time-olap-databases-for-sub-second-product-analytics","title":"Best real-time OLAP databases for sub-second product analytics","question":"What are the best real-time OLAP databases for sub-second product analytics in 2026?","verdict":"As of 2026-08-06, ChatGPT, Claude and Gemini collectively rank ClickHouse #1 for real-time olap databases for sub-second product analytics on ModelsAgree — unanimous among the 3 models that have answered. The models' case: Best overall value for append-heavy product events: exceptional scan and aggregation speed, high-cardinality SQL, strong compression, materialized views, broad. The models' main caveat: Updates, deletes, and deduplication are less natural than append-only ingestion, so it is not ideal when mutable records require immediate. The strongest alternative is Apache Pinot — Purpose-built for user-facing analytics, with query-on-arrival streaming ingestion, consistently low latency at extreme concurrency, rich indexing. Source: https://modelsagree.com/best/best-real-time-olap-databases-for-sub-second-product-analytics (modelsagree.com, CC BY 4.0).","category":"Database","url":"https://modelsagree.com/best/best-real-time-olap-databases-for-sub-second-product-analytics","updated":"2026-08-06","models":["ChatGPT","Claude","Gemini"],"consensus":"All 3 models rank ClickHouse the top pick","disagreement":null,"combined":[{"rank":1,"product":"ClickHouse","domain":"clickhouse.com","score":15,"appearances":3,"modelRanks":{"ChatGPT":1,"Claude":1,"Gemini":1},"reason":"Best overall value for append-heavy product events: exceptional scan and aggregation speed, high-cardinality SQL, strong compression, materialized views, broad integrations, and excellent managed or self-hosted paths. Near-tied with Pinot; it wins on versatility and ecosystem."},{"rank":2,"product":"Apache Pinot","domain":"apache.org","score":12,"appearances":3,"modelRanks":{"ChatGPT":2,"Claude":2,"Gemini":2},"reason":"Purpose-built for user-facing analytics, with query-on-arrival streaming ingestion, consistently low latency at extreme concurrency, rich indexing, tenant-aware routing, and production-tested upserts. It can beat ClickHouse for tightly defined high-QPS product APIs."},{"rank":3,"product":"StarRocks","domain":"starrocks.io","score":9,"appearances":3,"modelRanks":{"ChatGPT":3,"Claude":3,"Gemini":3},"reason":"Combines fast vectorized OLAP with excellent multi-table joins, a strong cost-based optimizer, transparent materialized-view rewrites, and high-performance primary-key updates. It is especially strong when product analytics mixes events with frequently changing customer or account data."},{"rank":4,"product":"Apache Druid","domain":"apache.org","score":6,"appearances":3,"modelRanks":{"ChatGPT":4,"Claude":4,"Gemini":4},"reason":"Mature streaming ingestion, query-on-arrival freshness, strong time filtering, bitmap indexes, rollups, and dependable concurrency make it excellent for event timelines, funnels, operational dashboards, and slice-and-dice workloads."},{"rank":5,"product":"Apache Doris","domain":"apache.org","score":1,"appearances":1,"modelRanks":{"ChatGPT":5},"reason":"Near-tied with StarRocks on value: approachable MySQL-compatible SQL, sub-second aggregations, strong concurrency, real-time unique-key updates, synchronous single-table materialized views, and useful lakehouse access."},{"rank":6,"product":"Hydrolix","domain":null,"score":1,"appearances":1,"modelRanks":{"Gemini":5},"reason":"Decoupled compute-and-storage architecture built natively on cloud object storage with extreme event data compression, enabling sub-second streaming queries on high-volume product logs at a fraction of typical RAM/disk infrastructure costs."},{"rank":7,"product":"Tinybird","domain":null,"score":1,"appearances":1,"modelRanks":{"Claude":5},"reason":"Managed ClickHouse tuned specifically for product analytics, turning queries into versioned, low-latency APIs with real-time ingestion — the fastest path for a product/engineering team to ship sub-second analytics without running infrastructure."}],"perModel":{"ChatGPT":[{"rank":1,"product":"ClickHouse","reason":"Best overall value for append-heavy product events: exceptional scan and aggregation speed, high-cardinality SQL, strong compression, materialized views, broad integrations, and excellent managed or self-hosted paths. Near-tied with Pinot; it wins on versatility and ecosystem.","fix":"Updates, deletes, and deduplication are less natural than append-only ingestion, so it is not ideal when mutable records require immediate transactional correctness."},{"rank":2,"product":"Apache Pinot","reason":"Purpose-built for user-facing analytics, with query-on-arrival streaming ingestion, consistently low latency at extreme concurrency, rich indexing, tenant-aware routing, and production-tested upserts. It can beat ClickHouse for tightly defined high-QPS product APIs.","fix":"It is a specialized, operationally demanding distributed system; complex joins, long-running SQL, and general ETL are not its sweet spot."},{"rank":3,"product":"StarRocks","reason":"Combines fast vectorized OLAP with excellent multi-table joins, a strong cost-based optimizer, transparent materialized-view rewrites, and high-performance primary-key updates. It is especially strong when product analytics mixes events with frequently changing customer or account data.","fix":"Predictable sub-second performance still depends heavily on thoughtful partitioning, bucketing, and materialized-view design, with a smaller ecosystem than ClickHouse."},{"rank":4,"product":"Apache Druid","reason":"Mature streaming ingestion, query-on-arrival freshness, strong time filtering, bitmap indexes, rollups, and dependable concurrency make it excellent for event timelines, funnels, operational dashboards, and slice-and-dice workloads.","fix":"Its timestamp-centric, denormalization-first model is restrictive; large datasource-to-datasource joins must generally happen before ingestion."},{"rank":5,"product":"Apache Doris","reason":"Near-tied with StarRocks on value: approachable MySQL-compatible SQL, sub-second aggregations, strong concurrency, real-time unique-key updates, synchronous single-table materialized views, and useful lakehouse access.","fix":"Real-time acceleration is weaker for complex multi-table transformations because asynchronous materialized views can lag and synchronous ones are single-table."}],"Claude":[{"rank":1,"product":"ClickHouse","reason":"Fastest general-purpose columnar OLAP engine, with vectorized execution, aggressive compression, and materialized views that make sub-second aggregations over billions of rows routine; enormous ecosystem, cheap to run, and now credible at real-time ingest with async inserts and the ReplacingMergeTree/upsert path much improved. Assumed the \"typical practitioner\" wants raw speed and cost efficiency over turnkey user-facing serving.","fix":"High-concurrency, per-user point-lookup workloads and frequent mutations still need real tuning and self-managed cluster ops — not the easiest for a small team wanting a hands-off, thousands-of-QPS user-facing service."},{"rank":2,"product":"Apache Pinot","reason":"Purpose-built for exactly this — user-facing, high-QPS, sub-second product analytics — with star-tree indexes, real-time upserts, and tiered storage; proven serving thousands of concurrent queries per second at LinkedIn/Uber scale with predictable p99 latency.","fix":"Operationally heavy (many moving components: controller, broker, server, plus ZooKeeper/deep store) and joins remain weaker than SQL-first engines — overkill and hard to run for smaller teams."},{"rank":3,"product":"StarRocks","reason":"Best-in-class distributed join performance with a cost-based optimizer, MySQL protocol, and strong real-time primary-key upserts, so it handles denormalized AND normalized product-analytics schemas without pre-joining everything; sub-second even on complex multi-table queries.","fix":"Younger ecosystem and community than ClickHouse/Druid, memory-hungry, and fewer battle-tested very-large deployments — more risk for conservative shops."},{"rank":4,"product":"Apache Druid","reason":"Mature, reliable real-time ingestion from Kafka with sub-second time-partitioned aggregations and strong operational track record for event/clickstream analytics; excellent for time-series-shaped product data.","fix":"Complex multi-service architecture, weak JOIN support, and a large resource footprint; feels dated next to ClickHouse/StarRocks for flexible SQL."},{"rank":5,"product":"Tinybird","reason":"Managed ClickHouse tuned specifically for product analytics, turning queries into versioned, low-latency APIs with real-time ingestion — the fastest path for a product/engineering team to ship sub-second analytics without running infrastructure.","fix":"Vendor lock-in and cost at high scale, and its performance ceiling is ClickHouse itself — a poor fit for teams needing full engine control or on-prem."}],"Gemini":[{"rank":1,"product":"ClickHouse","reason":"Unmatched raw columnar scan speeds, native behavioral analytics functions (windowFunnel, retention, sequence matching), and standard-bearer status powering modern event-based product analytics; assumes append-only event log workflows. Near-tie with Apache Pinot for #1.","fix":"Poor performance on real-time primary-key upserts and complex multi-table joins, making it unfit for highly normalized schemas or heavily mutable state."},{"rank":2,"product":"Apache Pinot","reason":"Purpose-built for ultra-low latency, sub-second aggregate queries under extreme query-per-second (QPS) concurrency using Star-Tree indexes; assumes user-facing analytics dashboards with high concurrent traffic. Near-tie with ClickHouse for #1.","fix":"High operational cluster management overhead and lack of native built-in product analytics primitives like funnel state machines."},{"rank":3,"product":"StarRocks","reason":"Vectorized execution engine with native primary-key real-time upserts and high-performance real-time joins, allowing sub-second product metrics over normalized event and dimension tables without pre-denormalization.","fix":"Higher RAM resource footprint per node compared to append-only engines, making it expensive for low-cost cold historical event archiving."},{"rank":4,"product":"Apache Druid","reason":"Battle-tested streaming ingestion integration with Apache Kafka alongside bitmap indexes, offering reliable sub-second slice-and-dice time-series product metrics at petabyte scale.","fix":"Multi-role architectural complexity (Broker, Historical, MiddleManager) and weak support for ad-hoc complex join queries across arbitrary non-time-series datasets."},{"rank":5,"product":"Hydrolix","reason":"Decoupled compute-and-storage architecture built natively on cloud object storage with extreme event data compression, enabling sub-second streaming queries on high-volume product logs at a fraction of typical RAM/disk infrastructure costs.","fix":"Proprietary commercial engine with a smaller integration ecosystem and lower raw performance on complex multi-table joins than in-memory vectorized engines."}]},"missedByModel":{"ChatGPT":[{"product":"SingleStore","reason":"excellent low-latency HTAP and mutable-data support, but proprietary cost and complexity weaken its value for ordinary append-heavy product analytics"},{"product":"Tinybird","reason":"outstanding managed developer experience and API delivery, but it is a ClickHouse-based platform rather than a comparably independent database engine"}],"Claude":[{"product":"Apache Doris","reason":"very close to StarRocks and a genuine near-tie for #3, but its optimizer and large-scale join maturity trail slightly"}],"Gemini":[{"product":"Tinybird","reason":"missed because it is a managed developer application platform built on top of ClickHouse rather than a standalone database engine"},{"product":"Rockset","reason":"missed because it was acquired by OpenAI in 2024 and sunset as a standalone commercial database offering"}]}}