ClickHouse vs Apache Druid

Compare and contrast ClickHouse and Apache Druid by architecture, ingestion, queries, performance, and scalability.

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ClickHouse vs Apache Druid Architecture

Architecture

ClickHouse

Apache Druid

Deployment model

• Self-managed on-premises or on cloud infrastructure • Several managed cloud services available

• SaaS or self managed. • Imply’s Druid cloud offering requires customers to configure, scale, and capacity plan

Use of storage hierarchy

• Designed to use hard disk drives for storage • Can also use SSD if available

• Queries are served from data on disk and an in-memory cache • Cloud storage or HDFS for deep storage

Isolation of ingest and query

• Ingestion and queries are run on the same node by default • The ingestion and querying processes can be run on separate nodes, however not for real-time data

Separation of compute and storage

No - although ClickHouse Cloud decouples compute and cloud storage

No, although Imply’s offering separates compute and storage

Isolation for multiple applications

ClickHouse is open source and can be deployed anywhere. Several vendors such as ClickHouse, the company, and Tinybird also offer cloud versions. Compute and storage are tightly coupled, although ClickHouse Cloud was rearchitected to decouple compute and storage. ClickHouse Cloud pricing is based on compute and storage usage.

Druid’s architecture employs nodes called data servers that are used for both ingestion and queries. High ingestion or query load can cause CPU and memory contention compared with Druid alternatives. Breaking apart the pre-packaged ingestion and query server components involves planning ahead and additional complexity, and is not dynamic.

ClickHouse vs Apache Druid Ingestion

Ingestion

ClickHouse

Apache Druid

Data sources

• Core integrations for ingestion from Kafka, S3, Google Cloud Storage • Other partner and community integrations available

Data connectors to: • Events streams (e.g. Kafka, Kinesis) • Data lakes (e.g. S3, Google Cloud Storage) • RDBMS and HDFS databases CDC events from databases require manual conversion to Druid events

Semi structured data

• JSON Object type for handling nested JSON • Automatically infers schema from a subset of rows

Druid requires flattening nested data at ingest and maintaining a flattening spec as the schema changes over time

Transformations and rollups

Yes - several storage engines can pre-aggregate data

Yes, using ingestion specs written in JSON support rollups and simple transformations. SQL ingest transformations available for Imply

ClickHouse has core integrations from common sources such as Kafka and S3. It recently introduced greater ability to handle semi-structured data using the JSON Object type and automatic schema inference.

Druid has built-in connectors that manage ingestion from common data sources. Unlike some Druid competitors, it doesn’t support nested data, so data must be flattened at ingest. Denormalization is also required at ingest, increasing operational burden for certain use cases.

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ClickHouse vs Apache Druid Performance

Performance

ClickHouse

Apache Druid

Updates

• Writes to immutable files • Updates rewrite and merge data files asynchronously • Frequent updates are not recommended due to potential for large rewrites

Druid segments become immutable once committed and published, making it more appropriate for append-only use cases

Indexing

• Primary indexes use sparse indexing on data ordered by primary key • Secondary data skipping indexes

• Bitmap index

Query latency

Sub-100ms to seconds, optimized for large-scale aggregations

Typically sub-second query latency for denormalized, flattened datasets up to 100s of TBs

Storage format

• Column-oriented • Heavily compressed to minimize storage footprint

Columnar format partitioned by time

Streaming ingest

Recommends inserting in batches of >1000 rows and <1 insert per second

• Sub 15 seconds

ClickHouse leverages column orientation and heavy compression for better performance on analytics workloads. It also uses indexing to accelerate queries as well. While ClickHouse use cases often involve streaming data from Kafka, batching data is recommended for efficient ingestion.

Druid is designed to make streaming data queryable as quickly as possible. JOINs are either impossible or incur a large performance penalty. Updates are only possible via batch jobs. Druid leverages data denormalization and write-time aggregation at ingestion to reduce query latency.

ClickHouse vs Apache Druid Queries

Queries

ClickHouse

Apache Druid

Joins

Yes

Yes, only for datasets that fit in memory, and with a query latency penalty

Query language

SQL

Druid native queries • Druid SQL

Developer tooling

• API for querying SQL via POST command • Python, Java, Node.js and Go language clients

• Druid SQL API

Visualization tools

Integrations with Metabase, Superset, Grafana, Tableau, Deepnote and Rocket BI

Pivot, maintained by Imply

ClickHouse uses SQL for querying, with support for SQL joins. ClickHouse integrates with some common tools for visual analytics, including Superset, Grafana and Tableau.

Druid has a native JSON-based query language and provides Druid SQL as an alternative that translates into its native queries. JOINs are not recommended.

ClickHouse vs Apache Druid Scalability

Scalability

ClickHouse

Apache Druid

Vertical scaling

Scale up single-node ClickHouse for vertical scaling

Users can manually increase or decrease server sizes

Horizontal scaling

• Compute and storage scaled in lockstep • Data rebalanced to populate newly added nodes • Cloud offerings automate some of the scaling and rebalancing effort

• Users can manually add additional nodes to a scaled-out cluster. • Imply automates some of the horizontal scaling process.

ClickHouse can be used in both single-node and distributed modes. Tight coupling of compute and storage and the need to rebalance data make scaling out more complex, but cloud versions of ClickHouse help automate this process.

Druid users are exposed to complex decisions about the number and size of servers as clusters are scaled.

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