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ClickHouse

ClickHouse is a fast open-source column-oriented database management system that allows generating analytical data reports in real-time using SQL queries

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Type
OLAP Database
Category
Data Warehouse
Pricing
Free (open source)
Deployment
Cloud or self-hosted
Last updatedSeptember 21, 2026Open Source

Editor's Take

We recommend ClickHouse for data teams that need sub-second, real-time analytical SQL on high-volume event or observability data and can operate an open-source database themselves. Its column-oriented architecture makes it a strong alternative to Snowflake for latency-sensitive analytics, but the provided context does not establish enterprise adoption, total operating cost, or managed-support requirements; start with a 2–5 person data platform team and benchmark it against your workload before standardizing.

— Egor Burlakov, Editor

Evaluate ClickHouse

ClickHouse Pricing
Plans, costs & free tier
Top Alternatives
Substitutes, and when to pick them
Best OLAP Databases
Ranked by public evidence
OLAP Databases landscape
7 published products in this market
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Every published tool in the category
Stack Recommender
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Popular comparisons

vs Apache Pinot
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vs Apache Druid
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vs StarRocks
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vs Imply Cloud
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See all 15 ClickHouse comparisons
vs SingleStore
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vs DuckDB
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vs InfluxDB
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vs Elasticsearch
Head-to-head comparison
vs Dremio
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vs Databricks
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vs Exasol
Head-to-head comparison
vs Amazon Redshift
Head-to-head comparison
vs Snowflake
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vs TimescaleDB
Head-to-head comparison
vs Google BigQuery
Head-to-head comparison

ClickHouse: product and architecture

Our verdict: ClickHouse is a strong choice for teams that need SQL analytics over very large, fast-moving datasets and can accept an OLAP-first data model. This ClickHouse review recommends it for real-time analytics, observability, and AI workloads that benefit from columnar storage, horizontal scaling, and millisecond-oriented query performance; avoid treating it as a general-purpose transactional database.

ClickHouse is an open-source, column-oriented database management system for generating analytical reports in real time with SQL queries. Its stated design target is linear scalability across trillions of rows and petabytes of data, while ClickHouse Cloud provides a scalable serverless option for teams that do not want to operate the database themselves.

Overview

ClickHouse positions itself as a real-time analytics database for modern data challenges, including dashboards, observability, data warehousing, and AI systems. The product website describes millisecond queries at petabyte scale and the ability to analyze billions of rows in real time. Those are ambitious operating targets, but they also explain the product’s focus: ClickHouse is built around analytical reads and aggregation, not broad transactional data management.

The core evaluator decision is straightforward. We recommend ClickHouse for data engineering and analytics teams with large event, log, metrics, trace, or application datasets that must remain queryable with low latency. It is especially relevant where query speed affects a user-facing dashboard, incident workflow, agentic application, or operational decision.

ClickHouse’s public repository describes it as a real-time analytics database management system. The repository is written primarily in C++, uses the Apache-2.0 license, has 49,229 GitHub stars, and its latest listed stable release is v26.7.3.19-stable, dated August 6, 2026. Its last listed repository push was August 13, 2026; these are useful public signals of activity and community attention, not proof of enterprise adoption or operational fit.

The product’s current positioning also extends beyond conventional analytics. ClickHouse states that Langfuse is now part of ClickHouse and describes it as an open-source platform for LLM observability, evaluations, and prompt management. That makes ClickHouse more relevant to teams collecting high-volume AI application telemetry, although the available data does not establish which Langfuse capabilities are included in each deployment or pricing option.

Key Features and Architecture

ClickHouse’s defining architectural choice is columnar storage. Instead of storing complete rows together, it stores data by column, which is well suited to read-heavy analytical workloads because a query can focus on the columns it needs. Column-oriented organization also supports more efficient compression, a meaningful advantage when large analytical datasets must remain available for repeated querying.

The database supports real-time analytics, meaning it is intended to process data and make it available for immediate analysis rather than waiting for a delayed reporting cycle. ClickHouse explicitly frames this capability around instant insights and dashboards at scale. The practical trade-off is that teams should model data and query patterns for analytics from the start, rather than expecting the flexibility of a database selected primarily for frequent row-level manipulation.

ClickHouse uses a distributed architecture designed for horizontal scaling across multiple nodes. The provided feature data identifies high availability and fault tolerance as outcomes of this design, while the product description states that the system can handle trillions of rows and petabytes of data. Distributed operation is valuable for sustained analytical workloads, but it raises the operational bar: teams need clear ownership of topology, replication, capacity, and failure handling when self-managing.

Key capabilities include:

  • SQL support: ClickHouse offers a rich SQL dialect, giving SQL-literate data engineers and analytics engineers a familiar interface for querying analytical data.
  • Data compression: It uses compression algorithms including LZ4 and ZSTD, intended to reduce storage costs and improve query performance.
  • Materialized views: Teams can pre-compute complex queries so frequently requested results are faster to access.
  • Data replication: Real users specifically identify data replication as a strength, reinforcing the value of ClickHouse’s distributed design for resilient analytical systems.
  • ClickHouse Cloud: The product description identifies a scalable serverless solution for real-time analytics, providing an alternative to operating the database infrastructure directly.
  • AI and observability positioning: The repository topics include ai, analytics, big-data, and cloud-native, while the website highlights agentic systems and ClickStack, its open-source observability stack powered by ClickHouse.

Materialized views are particularly important for workload design. They can improve access to repeatedly requested complex results, but they require teams to decide in advance which query patterns justify pre-computation. This is a deliberate performance trade-off: ClickHouse can accelerate known analytical paths, but it does not remove the need for disciplined schema and workload design.

Ideal Use Cases

ClickHouse is best for a data platform team supporting real-time product or business analytics over very large datasets. For example, a team of 5 to 15 data engineers and analytics engineers operating dashboards over billions of event records can use its columnar storage and SQL interface to support fast aggregation-oriented analysis. The value is strongest when reporting latency matters to the business rather than merely improving an overnight batch job.

A second strong fit is observability. An engineering organization collecting logs, metrics, and traces can use ClickHouse’s stated observability focus and ClickStack positioning to store and query telemetry at scale. This is particularly relevant when incident responders need to investigate high-volume operational data without waiting for a separate reporting pipeline.

A third fit is AI application monitoring and analytics. Teams building agentic systems can evaluate ClickHouse where they need to analyze application behavior at scale and where LLM observability, evaluations, and prompt management are part of the operating model. The Langfuse acquisition-related product messaging makes this a strategically relevant area, but teams should validate the exact product boundaries and deployment requirements with ClickHouse before standardizing.

Finance and e-commerce are also cited in third-party review material as data-driven industries where ClickHouse fits well. These sectors often need current analytical insight from high-volume data, and the external review specifically characterizes ClickHouse as useful for analysts and IT teams that need high-speed OLAP. We would use that as a starting point for evaluation, not as a substitute for testing representative queries and data volumes.

Don’t use ClickHouse if your primary requirement is frequent, general-purpose data manipulation. User feedback explicitly identifies data manipulation as a weakness, and the available product information consistently describes ClickHouse as an OLAP and real-time analytics system. Choose a database designed around transactional application workflows instead if inserts, updates, and operational record management are the central workload.

Strengths & Trade-offs

ClickHouse has credible strengths for its intended analytical workload, but the strongest advantages are inseparable from the product’s specialization. The available user feedback gives it a 7.1/10 rating from 9 reviews, which is useful sentiment but a limited sample. We would treat it as directional evidence: users value performance, configuration, and replication, while data manipulation is a concrete concern.

Pros

  • High-performance analytical orientation: Users identify high performance as a strength, and ClickHouse is explicitly designed for real-time analytical reporting through SQL queries.
  • Columnar storage for read-heavy workloads: Storing data by columns supports efficient access to selected fields and enables compression-oriented analytical design.
  • Compression support with named algorithms: LZ4 and ZSTD are specifically identified as supported compression approaches, helping reduce storage demands while supporting performance goals.
  • Distributed scalability and resilience: The architecture is designed to scale horizontally across multiple nodes and provide high availability and fault tolerance; users also name data replication as a strength.
  • Pre-computation through materialized views: Teams can accelerate frequently queried complex results rather than recomputing every analytical query from raw data.
  • Open-source foundation: Apache-2.0 licensing and a free, open-source database option make ClickHouse practical for teams that want deployment control.

Cons

  • Weak fit for data manipulation: Real users explicitly identify data manipulation as a weakness, so ClickHouse should not be selected as the default operational database for workflows centered on changing individual records.
  • Distributed systems complexity: Horizontal scaling, replication, and fault tolerance are valuable, but self-managed deployments require operational competence in multi-node database management.
  • Performance requires intentional workload design: Columnar storage and materialized views reward read-heavy, predictable analytical patterns; teams cannot assume every SQL workload will benefit equally.
  • Cloud cost visibility is incomplete in the supplied official data: The official pricing text provides options and a free trial, but not enough current detail to model cloud spend from first principles.
  • Review evidence is limited: The 7.1/10 score is based on only 9 reviews, so it should not be over-weighted when assessing usability, support, or operational maturity.

The key trade-off is clear: ClickHouse offers speed, scale, and compression for analytical workloads by being purpose-built for them. That is a better bargain than a generalist database when analytical queries dominate, but it is a poor bargain when the organization needs broad transactional behavior first and analytics second.

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Alternatives to ClickHouse

The reviewed substitutes for ClickHouse among the OLAP databases, and what would make each one the better answer.

Direct alternatives

Reviewed substitutes: products bought for the same job, where a team picks one.

Apache Pinot
Choose this if you are building user-facing analytical applications that demand extreme concurrency and consistently low latencies.
Apache Druid
Choose this if you need real-time ingestion from streaming sources combined with sub-second interactive queries at high concurrency levels.
StarRocks
Choose this if you want ClickHouse-level performance with easier operability and native MySQL wire protocol support.Applies to: Choosing the analytical database that will serve fast queries over large event data.
Imply Cloud
Two products of the same kind answering one purchase. They are compared directly in buyer's guides and vendor head-to-heads, and a team adopts one.Applies to: Choosing between two products of the same kind for one job.
SingleStore
Both serve fast analytical queries over large datasets and are compared directly on db-engines and in 2026 OLAP guides. SingleStore optimises joins and real-time ingestion and speaks the MySQL protocol; ClickHouse optimises raw columnar throughput. A team picks one engine.Applies to: Choosing the analytical database that will serve fast queries, with or without heavy joins.

Other approaches

A different approach to the same problem. Each substitutes only for the workload named beside it.

DuckDB
Choose this if you need fast local analytics on datasets that fit on a single machine, or you want to embed OLAP capabilities directly in Python, R, or Java applications without running a separate server.Applies to: Single-node or in-process analytical queries, where an embedded engine can replace a cluster. Not an alternative for multi-tenant serving.
InfluxDB
Both can answer the same need from different starting points, with overlapping but not identical scope, so the decision is how the stack is shaped rather than which product is better. Teams compare them directly and many run both, each covering the part it is stronger at.Applies to: Deciding how the stack is shaped, where both products can be part of the answer.
Elasticsearch
A columnar analytical database and a search engine both answer questions over large volumes, one by aggregation and one by search and relevance ranking. The overlap on log and event analytics is real, which is why they are compared, and the decision follows whether the workload is aggregation or retrieval.Applies to: Whether the workload is aggregation over events or search and relevance over documents.
Dremio
A lakehouse platform and an analytical database reach queries by different architectures: platform breadth across engineering, SQL and ML against query latency and concurrency. Published comparisons frame it that way and many organisations run both.Applies to: Serving low-latency analytical queries, and whether one platform must also carry ETL and ML.
Amazon Redshift
A cloud warehouse and an analytical database answer the same SQL question by different architectures: platform breadth, governance and ecosystem against query latency and concurrency. Published comparisons frame the choice that way, and many organisations run both, with the warehouse as the central store and the analytical database serving fast queries.Applies to: Serving fast analytical queries, and whether the central warehouse can also carry them.
Databricks
A lakehouse platform and a columnar OLAP database reach analytical queries by different architectures. Independent comparisons frame it as lakehouse breadth against query latency, and many organisations run both, which makes it an architecture choice rather than a substitution.Applies to: Serving fast analytical queries, and whether the same platform must also carry ETL and ML.
Exasol
Two analytical databases that reach low latency differently: Exasol holds working data in memory with self-tuning indexes and is bought as capacity, ClickHouse is columnar with explicit index and engine choices and is commonly self-hosted. The generated 'complementary' proposal is wrong; these are alternatives for one workload.Applies to: Choosing an analytical database for concurrent interactive queries.

Head to head

See detailed alternatives analysis

ClickHouse is one of the fastest columnar OLAP databases available, processing petabyte-scale analytical queries in milliseconds using its column-oriented storage engine written in C++. With nearly 47,000 GitHub stars and over 100,000 developers using the platform, it has become a go-to choice for real-time analytics. However, ClickHouse's complexity in data manipulation, limited transaction support, and steep operational overhead for self-hosted deployments mean that several ClickHouse alternatives deserve serious consideration depending on your workload profile.

Top Alternatives Overview

DuckDB is an in-process SQL OLAP database that runs entirely embedded within your application, requiring zero infrastructure setup. It uses a columnar-vectorized query execution engine and supports Parquet, S3, and standard SQL natively. DuckDB earned a 9/10 user rating and excels at single-node analytical workloads where ClickHouse's distributed architecture adds unnecessary complexity. Choose this if you need fast local analytics on datasets that fit on a single machine, or you want to embed OLAP capabilities directly in Python, R, or Java applications without running a separate server.

Apache Druid is a distributed real-time analytics data store that combines concepts from data warehouses, time-series databases, and search systems. Druid ingests streaming data from Kafka and provides sub-second OLAP queries with automatic data summarization and indexing. It handles high-concurrency workloads efficiently through its segment-based architecture and pre-aggregation at ingestion time. Choose this if you need real-time ingestion from streaming sources combined with sub-second interactive queries at high concurrency levels.

Apache Pinot is a real-time distributed OLAP datastore rated 9/10 by users, designed specifically for low-latency user-facing analytics. It powers analytics at LinkedIn, Uber, and Stripe, delivering P90 query latencies in tens of milliseconds even on petabyte-scale datasets. Pinot supports hundreds of thousands of concurrent queries per second with pluggable indexing options including StarTree, Bloom filter, and geospatial indexes. Choose this if you are building user-facing analytical applications that demand extreme concurrency and consistently low latencies.

Trino (formerly PrestoSQL) is a distributed SQL query engine designed for federated analytics across multiple data sources. Unlike ClickHouse, which requires data ingestion, Trino queries data in place across Hadoop, S3, MySQL, Cassandra, and other systems within a single SQL query. The community edition is free and open-source under Apache 2.0, with a cloud version starting at $12/month. Choose this if you need to query data across multiple heterogeneous sources without copying or moving it into a centralized store.

StarRocks is a next-generation sub-second MPP OLAP database that won InfoWorld's 2023 BOSSIE Award, designed for multi-dimensional analytics, real-time analytics, and ad-hoc queries. It is free and open source, and managed offerings are sold separately by third parties. StarRocks provides MySQL protocol compatibility, making migration from existing MySQL-based tooling straightforward. Choose this if you want ClickHouse-level performance with easier operability and native MySQL wire protocol support.

PostgreSQL is the most mature open-source relational database with over 30 years of active development, offering JSONB support, full-text search, and extensive extensibility. While not a dedicated OLAP engine, PostgreSQL with extensions like Citus or TimescaleDB can handle moderate analytical workloads alongside transactional ones. Choose this if your analytical needs are secondary to transactional workloads and you want a single database that handles both OLTP and moderate OLAP queries.

Architecture and Approach Comparison

ClickHouse uses a shared-nothing distributed architecture with columnar storage, MergeTree table engines, and aggressive compression to achieve its query speed. It processes data using vectorized execution and SIMD instructions, reading only the columns needed for each query. This makes it exceptionally fast for aggregation-heavy workloads but limits its ability to handle frequent updates and deletes -- a well-known weakness users cite around data manipulation.

DuckDB takes the opposite architectural approach: it runs as an embedded, single-process engine with no network overhead. While ClickHouse requires a running server process and cluster management for distributed deployments, DuckDB embeds directly into your application process. This means DuckDB wins on simplicity and startup time but cannot scale horizontally across nodes.

Apache Druid and Apache Pinot both use segment-based architectures optimized for time-series and event data. Druid pre-aggregates data at ingestion time using rollup, reducing storage requirements and query latency at the cost of losing raw row-level detail. Pinot preserves raw data while using pluggable indexes (inverted, range, text, JSON, geospatial) to accelerate queries. Both handle streaming ingestion from Kafka natively, whereas ClickHouse's Kafka integration requires more configuration and tuning.

Trino is architecturally different from all others here because it is a query engine, not a storage engine. It pushes computation down to the underlying data sources and federates results. This makes it ideal for data lake query federation but means it depends on the performance characteristics of each connected source system.

StarRocks uses a hybrid architecture combining columnar storage with an MPP execution engine and a cost-based optimizer. It supports both real-time ingestion and batch loading, and its MySQL wire protocol compatibility means existing MySQL clients, BI tools, and JDBC/ODBC connectors work without modification.

Pricing Comparison

Most ClickHouse alternatives are open-source, but their managed and cloud offerings differ substantially in cost structure.

ToolSelf-Hosted CostCloud/Managed Starting PricePricing Model
ClickHouseFree (Apache 2.0)Usage-based (ClickHouse Cloud)Open Source + Cloud
DuckDBFree (MIT)N/A (embedded only)Open Source
Apache DruidFree (Apache 2.0)Vendor-dependent (Imply)Open Source
Apache PinotFree (Apache 2.0)Vendor-dependent (StarTree)Open Source
TrinoFree (Apache 2.0)From $12/monthOpen Source + Cloud
StarRocksFreeFrom $1,200/month (paid tier)Free tier + Paid
PostgreSQLFreeVendor-dependentOpen Source
ElasticsearchFree (basic)From $95/monthFreemium
DremioN/AFrom $0.20 per query (usage-based)Usage-Based

ClickHouse Cloud uses consumption-based pricing, which can be cost-effective for bursty workloads but unpredictable at scale. DuckDB stands out as truly free with no managed service to pay for. For teams wanting managed real-time OLAP, StarRocks' $1,200/month entry point and Elasticsearch's $95/month tiers provide more predictable costs. Dremio charges per query at $0.20, which suits infrequent analytical workloads but adds up quickly under heavy usage.

When to Consider Switching

Your workload is single-node analytical processing. If your datasets fit on a single machine (up to hundreds of gigabytes), ClickHouse's distributed architecture adds unnecessary operational burden. DuckDB delivers comparable columnar query performance with zero infrastructure, running embedded in your application.

You need strong data manipulation and transactional guarantees. ClickHouse's append-optimized MergeTree engine makes updates and deletes expensive and eventually consistent. PostgreSQL or StarRocks provide proper UPDATE/DELETE support with transactional semantics that ClickHouse cannot match.

Your primary use case is federated querying across data sources. If your data lives across S3, MySQL, Cassandra, and Hadoop, Trino lets you query all of them with a single SQL statement without ingesting data into ClickHouse first. This eliminates ETL pipelines and data duplication.

You are building user-facing applications requiring extreme concurrency. Apache Pinot handles hundreds of thousands of concurrent queries per second with P90 latencies in tens of milliseconds, purpose-built for user-facing dashboards. ClickHouse can handle high throughput but is optimized for complex analytical queries rather than massive concurrent simple lookups.

Your team needs streaming-first real-time analytics. While ClickHouse supports Kafka ingestion, Apache Druid and Apache Pinot were designed from the ground up for streaming data, offering tighter Kafka, Pulsar, and Kinesis integration with less configuration overhead.

Migration Considerations

ClickHouse uses standard SQL with extensions, so most analytical queries translate directly to alternatives like Trino, StarRocks, and DuckDB with minimal rewriting. StarRocks is the easiest migration target because it supports MySQL wire protocol and offers similar columnar storage semantics, meaning existing BI tools and connectors work without changes.

Data format compatibility is generally strong across this ecosystem. ClickHouse can export to Parquet, which DuckDB, Trino, Dremio, and Apache Pinot all read natively. For large datasets, exporting ClickHouse tables to Parquet files on S3 and importing from there is the most practical migration path.

The learning curve varies significantly. DuckDB requires almost no operational learning since it runs embedded. PostgreSQL is the most widely known database, so most teams already have expertise. Trino and Dremio add federation complexity but use standard ANSI SQL. Apache Druid and Apache Pinot have their own ingestion specifications and segment management concepts that require dedicated learning time, typically two to four weeks for a team to become productive.

One critical consideration: ClickHouse's MergeTree family of table engines (ReplacingMergeTree, AggregatingMergeTree, etc.) encode data modeling decisions that do not have direct equivalents in most alternatives. Teams heavily relying on these engine-specific features will need to redesign their data models during migration, which can take weeks of planning and testing for production workloads.

What users say about ClickHouse

Historical review enrichment from TrustRadius.

Pros

  • High performance
  • Easy configuration
  • Data replication

Public signals

About these signals

Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.

38.7k GitHub commits 90d50.0k GitHub stars0 vulnerabilities across 2 packagesOpenSSF score 4.4/10

See all signals from 10 sources
Source
Signals
Last updated
GitHub
Commits 90d:38.7k↑1.1kStars:50.0k↑120
September 21, 2026
Docker Hub
Pulls:301.2M↑5.3M
September 21, 2026
PyPI
Weekly downloads:6.5M↑546.4k
September 21, 2026
npm
Weekly downloads:2.5M↑2.2k
September 21, 2026
Google Trends
Search interest:Top 14%overallTop 19%in Data Warehouse
September 21, 2026
Hacker News
Matching stories, 90d:156
September 21, 2026
Product Hunt
Comments:0Rating:5.0/5Reviews:28Votes:12
September 21, 2026
Stack Overflow
Questions:2.2k
September 21, 2026
OSV
Package vulnerabilities:0 vulnerabilitiesacross 2 packages

npm · @clickhouse/client@1.23.1 · PyPI · clickhouse-connect@1.8.0

September 21, 2026
OpenSSF
Security score:4.4/10

github.com/ClickHouse/ClickHouse

September 21, 2026

Discussed on Hacker News

Recent Hacker News threads mentioning ClickHouse.

Frequently asked questions

What is ClickHouse?

ClickHouse is an open-source, column-oriented OLAP database management system designed for fast and efficient data processing.

Is ClickHouse free?

Yes, ClickHouse is completely free to use, being an open-source solution. This means you can download and install it without incurring any licensing fees.

How does ClickHouse compare to Apache Cassandra for big data analytics?

ClickHouse is optimized for analytical queries and provides quick query performance compared to Apache Cassandra, which is geared towards real-time data processing. However, both tools can be used in different scenarios depending on your specific needs.

Can I use ClickHouse with my existing MySQL database?

Yes, ClickHouse supports importing data from various sources, including MySQL, using its built-in import tool or external scripts. This allows you to leverage your existing database infrastructure while benefiting from ClickHouse's analytical capabilities.

Is ClickHouse suitable for real-time analytics and reporting?

While ClickHouse excels at handling complex queries and large datasets, it may not be the best fit for low-latency real-time analytics due to its focus on batch processing. However, you can use it in conjunction with other tools or services to achieve your desired performance levels.

What is the storage format used by ClickHouse?

ClickHouse stores data in a columnar format, which allows for efficient compression and query optimization. This format also enables fast querying and aggregation of large datasets.

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