ClickHouse vs Elasticsearch

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

Compare ClickHouse to Rockset here

Compare Elasticsearch to Rockset here

ClickHouse vs Elasticsearch Architecture

Architecture

ClickHouse

Elasticsearch

Deployment model

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

On-prem, PaaS options

Use of storage hierarchy

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

• Hot, warm and cold storage on disk • Frozen storage on cloud storage

Isolation of ingest and query

No - There are dedicated ingestion nodes but indexing, compaction and updates occur on the data nodes

Separation of compute and storage

No - although ClickHouse Cloud decouples compute and cloud storage

Isolation for multiple applications

Full isolation with replication

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.

Elasticsearch is an open-source distributed search engine built on Apache Lucene, a full text search library. Elasticsearch is a distributed system, which means that it is designed to operate across multiple nodes, each responsible for a part of the data.

ClickHouse vs Elasticsearch Ingestion

Ingestion

ClickHouse

Elasticsearch

Data sources

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

• Logstash JDBC input plugin for relational databases • Open-source Kafka plugin or Kafka Elasticsearch Service Sink Connector (available only to managed Confluent and Elasticsearch) • REST APIs or client libraries to sync data directly from the application

Semi structured data

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

Yes- Ingests JSON and XML without a predefined schema

Transformations and rollups

Yes - several storage engines can pre-aggregate data

Yes - Ingest pipelines can be configured to remove fields, extract values from text and enrich data. Ingest pipelines require ingest nodes in the cluster. Rolling up historical data is in technical preview

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.

Elasticsearch has a number of integrations as well as a REST API. It is a NoSQL database and natively supports semi-structured data. Transformations typically occur upstream so that data can be modeled for optimal performance before it is indexed in Elasticsearch.

ClickHouse vs Elasticsearch Performance

Performance

ClickHouse

Elasticsearch

Updates

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

Update API can update, delete or skip modifying the document. The entire document must be reindexed; in-place updates are not supported

Indexing

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

Inverted index

Query latency

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

50-1000ms queries on 100s of TBs

Storage format

• Column-oriented • Heavily compressed to minimize storage footprint

JSON documents

Streaming ingest

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

• Ingests on a per-record or batch basis • Data latency on a per-record basis is typically 1-2 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.

Elasticsearch is a search engine that utilizes an inverted index. Although this approach leads to storage amplification, it also enables low-latency queries that demand less computation. Elasticsearch is tailored to accommodate large scale, append-only data such as logs, events, and metrics. To manage frequently updated data, users often utilize the Bulk API to minimize computational costs and ensure consistent query performance.

ClickHouse vs Elasticsearch Queries

Queries

ClickHouse

Elasticsearch

Joins

Yes

No- Need to use workarounds including data denormalization, application-side joins, nested objects or parent-child relationships

Query language

SQL

DSL - domain specific language

Developer tooling

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

• REST API • Java, Javascript, Go, .NET, PHP, Perl, Python, Ruby, Rust

Visualization tools

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

• Kibana • PowerBI, Qlik, Tableau

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

Elasticsearch has its own domain specific language (DSL) based on JSON. Joins are not a first class citizen in Elasticsearch requiring a number of complex and expensive workarounds. Elasticsearch is known for its developer tooling and supports a number of client libraries. Kibana is the visualization layer for Elasticsearch and is frequently used for log analytics and monitoring.

ClickHouse vs Elasticsearch Scalability

Scalability

ClickHouse

Elasticsearch

Vertical scaling

Scale up single-node ClickHouse for vertical scaling

Manually resize machines

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

• Elasticsearch is horizontally scalable and can scale by adding nodes to the cluster • When using managed Elastic, autoscaling policies can be used to self-monitor cluster health and it is the responsibility of the operator to update resource allocations either manually or using APIs. Elasticsearch rebalances the data automatically obeying shard allocation rules • There are many cluster-level operations that need to be monitored when scaling

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.

Elasticsearch is horizontally scalable and can scale by adding more nodes to the cluster. Its tightly coupled architecture means that compute and storage scale together for performance. This often results in resource contention and overprovisioning. Scaling Elasticsearch often requires deep expertise as there are many levels of the system that need to be managed- the server, operating system, network and software.