When you're building real-time analytics into your application, database performance isn't just about raw speed. It's about whether your database can handle thousands of concurrent users hitting dashboards at the same time, or whether it can crunch through complex queries that join multiple tables and compute running aggregations.
ClickHouse and Apache Pinot are both columnar OLAP databases designed for analytics, but they optimize for fundamentally different workloads. This article compares their performance characteristics, architecture trade-offs, and operational complexity to help you choose the right database for your use case.
Performance benchmark summary
Apache Pinot delivers ultra-low latency for user-facing analytics with high query concurrency, often responding in single-digit milliseconds even under heavy load. ClickHouse excels at complex analytical workloads that scan large datasets and perform sophisticated aggregations. The choice between them comes down to whether your application prioritizes point queries with massive concurrency or deep analytical processing.
1. Latency under high concurrency
Pinot is built to handle hundreds of thousands of queries per secondPinot is built to handle hundreds of thousands of queries per second with consistent sub-10ms response times. The database achieves this through aggressive indexing strategies and a segment-based architecture that distributes query load across multiple servers.
ClickHouse performs well for concurrent queries but experiences more latency increase as concurrency growsClickHouse performs well for concurrent queries but experiences more latency increase as concurrency grows, particularly for queries that scan large portions of tables. For applications like real-time dashboards serving thousands of simultaneous users, Pinot typically maintains lower and more predictable latencies.
- Pinot's strength: Maintains 5-10ms p99 latency even at 100,000+ QPS for simple aggregations
- ClickHouse's strength: Handles 1,000-10,000 QPS with 50-200ms latency for more complex analytical queries
- Concurrency impact: Pinot's distributed query processing keeps latency flat as concurrency increases, while ClickHouse latency grows more linearly
2. Throughput on wide scans
ClickHouse excels when queries scan millions or billions of rows and perform complex aggregations. Its columnar storage format and vectorized query execution process data faster than Pinot for analytical workloads.
Pinot is optimized for queries that touch smaller subsets of data using indexes. When queries require full table scans or complex joins, ClickHouse typically completes them faster with better resource efficiency.
3. Compression and storage footprint
Both databases use columnar storage, but ClickHouse generally achieves better compression ratios for analytical data. ClickHouse's codec system allows per-column compression tuning, often resulting in 10-20x compression for typical event data.10-20x compression10-20x compression for typical event data.
Pinot uses a fixed compression strategy optimized for query speed rather than storage efficiency. This trade-off means Pinot tables typically consume 30-50% more disk space than equivalent ClickHouse tables, though query performance on that data is faster for indexed lookups.
Ingestion paths for real time analytics
Real-time data freshness determines how quickly new data becomes available for queries. Both databases support streaming and batch ingestion, but with different latency characteristics and operational complexity.
1. Streaming via Kafka
Pinot offers native Kafka integration with near-immediate data availability, typically making records queryable within 1-5 seconds of arrival in Kafka. The database consumes Kafka topics directly and builds queryable segments in real time without requiring external processing.
ClickHouse supports Kafka through the Kafka table engine, which provides reliable ingestion but with slightly higher latency. Records typically become queryable within 5-30 seconds depending on batch size and flush interval configuration.
2. Batch loads from object storage
ClickHouse has native support for reading from S3, GCS, and Azure Blob Storage using table functions like s3() and gcs(). You can query files directly or load them into tables with simple INSERT statements, making batch imports straightforward.
Pinot requires more configuration for batch ingestion from object storage. You typically set up batch ingestion jobs that read files, process them into Pinot's segment format, and upload those segments to deep storage.
3. Handling upserts and deletes
Upserts are operations that update existing records or insert new ones if they don't exist. ClickHouse has mature support for updates and deletes through the ReplacingMergeTree and CollapsingMergeTree table engines, making it suitable for use cases that require mutable data.
Pinot was designed as an append-only system, though recent versions have added experimental upsert support. The upsert implementation in Pinot requires careful configuration and has performance implications, particularly for high-volume update workloads.
Storage and indexing differences
The way databases store and index data fundamentally affects query performance. Understanding these differences helps you choose the right database for your query patterns.
1. Columnar format internals
Both databases store data in columns rather than rowsBoth databases store data in columns rather than rows, which improves compression and allows queries to read only the columns they need. However, their on-disk layouts differ significantly.
ClickHouse uses the MergeTree engine family, which organizes data into parts that are periodically merged in the background. Each part contains sorted data, and ClickHouse uses a sparse primary key index to quickly locate relevant data ranges.
Pinot uses a segment-based architecture where data is divided into immutable segments. Each segment contains multiple indexes (inverted, sorted, range) for different columns, allowing extremely fast lookups for specific values.
2. Index types and secondary indexes
Pinot offers more diverse indexing options than ClickHouse, including inverted indexes for text search, range indexes for numeric filtering, and star-tree indexes for pre-aggregated rollups. Star-tree indexes are specialized data structures that pre-compute aggregations for specific dimension combinations, enabling the sub-10ms query latencies Pinot is known for. (a0(reducing latency to 4ms in production workloads).
ClickHouse relies primarily on its sparse primary key index and skip indexes. Skip indexes help prune data during scans but don't provide the same lookup speed as Pinot's inverted indexes.
3. Tiered storage and cold data
ClickHouse supports hot/cold storage tiers natively through storage policies. You can configure tables to automatically move older data to cheaper storage like S3 while keeping recent data on fast local SSDs.
Pinot requires external tools and custom workflows to implement data lifecycle management. While Pinot can store segments in deep storage like S3, moving data between tiers and managing the lifecycle isn't as straightforward as ClickHouse's built-in tiering.
SQL feature depth and join support
The SQL features a database supports determine which analytical questions you can answer directly in queries versus requiring pre-computation or application-side processing.
1. Multi table joins
ClickHouse supports the full range of SQL join typesClickHouse supports the full range of SQL join types including INNER, LEFT, RIGHT, FULL OUTER, and CROSS joins. You can write complex queries that join multiple tables with nested subqueries, making it suitable for ad-hoc analytical exploration.
Pinot has limited join capabilities. While recent versions support basic joins, they come with restrictions on join types and performance characteristics. Pinot is designed for single-table queries with pre-joined or denormalized data.
2. Window functions
Window functions allow calculations across rows related to the current row, like computing running totals or ranking within groups. ClickHouse has comprehensive window function support including ROW_NUMBER, RANK, LAG, LEAD, and custom window frames.
Pinot supports basic window functions but with limitations on frame specifications and ordering. Complex analytical queries that rely on window functions often run into these limitations in Pinot.
3. Materialized views and rollups
Both databases support pre-aggregating data for faster queries, but they use different approaches. ClickHouse materialized views are actual tables that are automatically updated as new data arrives, using the same SQL you'd write for regular queries.
Pinot uses star-tree indexes, which are specialized data structures that pre-compute aggregations for specific dimension combinations. Star-tree indexes are faster for the exact queries they're designed for but less flexible than ClickHouse's materialized views.
Cluster topology and scaling effort
The operational complexity of running a database in production affects how quickly you can deploy it and how much engineering time you'll spend maintaining it.
1. Node roles and replication
ClickHouse has a simpler architecture with fewer moving parts. A basic ClickHouse cluster consists of shard nodes that store data and replica nodes for high availability. All nodes can handle both reads and writes, and coordination happens through ZooKeeper or ClickHouse Keeper.
Pinot requires multiple component types: controllers manage cluster metadata, brokers route queries, servers store and query data, and minions handle offline jobs. This distributed architecture provides flexibility but increases operational complexity, particularly for smaller deployments.
2. Elastic autoscaling
Pinot's architecture separates compute and storage, making it easier to scale query capacity independently of data storage. You can add broker and server nodes without moving data, which supports elastic scaling for variable query loads.
ClickHouse scaling typically involves adding new shards and rebalancing data, which is more manual and time-consuming. While ClickHouse Cloud and managed services like Tinybird handle this complexity for you, self-hosted ClickHouse requires more planning for scaling operations.
3. Backup and upgrades
ClickHouse backup and restore operations are straightforward. You can use built-in backup commands or file-system snapshots, and upgrades typically involve replacing binaries and restarting nodes.
Pinot's distributed architecture complicates backup and upgrade procedures. You coordinate backups across multiple component types, and upgrades require careful sequencing to maintain cluster availability.
Cost model and total cost of ownership
The total cost of running a database includes infrastructure, engineering time, and operational overhead. Different workloads favor different databases from a cost perspective.
1. Hardware efficiency
ClickHouse generally requires less hardware for the same analytical workload compared to Pinot. Its efficient columnar storage and vectorized query execution mean you can process more data with fewer CPU cores and less memory.
Pinot's indexing strategy trades storage and memory for query speed. The multiple indexes Pinot maintains increase storage requirements by 30-50%, and the in-memory components require more RAM for optimal performance.
2. Engineering hours to production
ClickHouse has a faster time to production for analytical use cases. You can install a single-node ClickHouse instance in minutes and start loading data immediately.
Pinot requires more upfront configuration. You set up multiple components, configure schemas with indexing strategies, and tune segment generation. This initial complexity pays off for user-facing analytics with strict latency requirements, but it extends the time to first query.
3. Cloud egress and storage tiering
Both databases support cloud deployment, but their cost structures differ. ClickHouse's better compression reduces storage costs, and its native tiering support lets you move cold data to cheaper storage automatically.
Pinot's higher storage requirements increase cloud storage costs, particularly for applications with large historical datasets. The lack of native tiering means you'll need custom solutions to manage data lifecycle and control costs.
When to choose ClickHouse vs Pinot
The right database depends on your specific requirements for latency, query complexity, operational simplicity, and team expertise.
Choose Pinot when your application serves user-facing analytics your application serves user-facing analytics with hundreds of thousands of concurrent users and requires consistent sub-10ms query latencies. Pinot excels at simple aggregations and filters on indexed dimensions, like powering real-time dashboards for customer-facing applications.
Choose ClickHouse when you support complex analytical queries including joins, window functions, and ad-hoc exploration. ClickHouse's simpler architecture and comprehensive SQL support make it better for internal analytics, data warehousing, and applications where query patterns evolve over time.
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Frequently asked questions about ClickHouse vs Pinot
How do you run an apples to apples benchmark between ClickHouse and Pinot?
Use identical datasets, query patterns, and hardware configurations while accounting for each database's optimization strengths. Focus on your specific use case rather than generic benchmarks, since Pinot will outperform ClickHouse for simple indexed lookups while ClickHouse will win for complex analytical queries.
Can you mix streaming and batch ingestion in the same table?
Both databases support hybrid ingestion patterns, but ClickHouse handles this more naturally through its MergeTree engine while Pinot requires careful segment management. In ClickHouse, you can insert streaming data from Kafka and batch data from S3 into the same table without additional configuration.
What is the learning curve for SQL differences between ClickHouse and Pinot?
ClickHouse uses standard SQL with some extensions, making it familiar to most analysts, while Pinot has a more limited SQL dialect optimized for OLAP queries. If your team already knows SQL, ClickHouse requires minimal learning.
How do SaaS limits affect multi tenant applications?
Managed services typically impose query rate limits and concurrent connection limits that may impact user-facing analytics applications requiring high throughput. Check whether the service meters by queries per second, data scanned, or both, and verify the limits align with your expected traffic.