Database Technology

ClickHouse for Analytics: A Beginner's Guide to Fast OLAP at Scale

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10 min read

This comprehensive guide is crafted for analytics engineers, developers, and data practitioners who have a basic understanding of SQL but are new to columnar OLAP systems. You will gain practical insights into ClickHouse, from understanding its core functionalities to efficiently modeling data, ingesting, and querying for optimal analytics performance. ClickHouse is increasingly popular due to its capabilities for handling large datasets and real-time analytics, making it a valuable asset for teams looking to enhance their analytics processes.

What is ClickHouse? A High-Level Overview

ClickHouse, originally developed at Yandex, serves as a powerful open-source OLAP DBMS optimized for rapid analytics on extensive event streams. Common use cases include:

  • Real-time dashboards and monitoring (time-series and event analytics).
  • Event logging and user behavior analytics based on large-scale clickstreams.
  • Ad-hoc analytics and BI queries processing billions of rows.
  • High-throughput metric stores focusing primarily on aggregations.

How ClickHouse Fits in the Data Ecosystem

  • Not designed for OLTP tasks involving many small transactional updates.
  • Distinct from traditional row-store data warehouses due to its column-oriented architecture and optimization for scanning and aggregations.
  • Can effectively complement cloud data warehouses or analytical lakes or even serve as a replacement for low-latency interactive analytics.

For additional practical guides and tips, explore Altinity’s blog.

Key Concepts: What Beginners Should Know

Columnar Storage

  • In a columnar storage system like ClickHouse, data for each column is stored separately. This design minimizes data scanning when only a few columns are queried.
  • Ideal for operations such as aggregations (e.g., GROUP BY, COUNT, SUM) that require only a subset of columns.

MergeTree Family

  • The MergeTree engines manage data using immutable files, continuously merging them in the background.
  • The ORDER BY clause defines the on-disk sort key, which is crucial for query performance.
  • PARTITION BY facilitates data segmentation, often based on date, optimizing query performance and speeding up deletions.

Compression and Codecs

  • ClickHouse supports multiple codecs, such as LZ4 (fast) and ZSTD (higher compression rates but with more CPU usage).
  • Choose codecs based on specific workload requirements and hardware capabilities.

Data Types and Functions

  • ClickHouse provides many analytical types and functions, including arrays, tuples, window functions, approximate algorithms (like HyperLogLog), and time-series helpers.

OLAP vs. OLTP Considerations

  • OLAP workloads are mostly append-heavy, making ClickHouse a suitable choice for such patterns.
  • For situations requiring frequent single-row updates, consider using other systems or MergeTree variants like CollapsingMergeTree or ReplacingMergeTree.

Getting Started: Installation, Clients, and Architecture Options

Installation Options

  • Docker: The simplest method for learning and local testing. Quickstart example:

    # Run a single-node ClickHouse
docker run -d --name clickhouse-server --ulimit nofile=262144:262144 -p 9000:9000 -p 8123:8123 clickhouse/clickhouse-server:latest

  • Native Packages: Available for Debian/Ubuntu or RPM installations (official installation docs).

  • Managed/Cloud: Explore ClickHouse Cloud or Altinity.Cloud for managed deployment options, which help reduce operational overhead.

Single Node vs Cluster

  • Single Node: Ideal for beginners and low-scale workloads.
  • Cluster: Use sharding to enhance read/write capacities and ensure high availability through replication. Careful planning of shards and replicas is essential.

Connection Methods

  • The HTTP API (port 8123) is straightforward for quick queries.
  • The native TCP protocol (port 9000) connects with clickhouse-client and various drivers.
  • JDBC/ODBC drivers facilitate integration with BI tools (e.g., Grafana).
  • Streaming ingestion via Kafka can be utilized (details discussed later).

Managed services simplify operational tasks but involve cost/performance trade-offs. Examine SLA, scaling options, and backup features during selection.

Data Modeling and Schema Design for Analytics

Denormalization and Schema Shape

  • Denormalized wide tables mitigate expensive joins, as ClickHouse excels in wide-table scans.
  • Aim to keep dimension tables small or use ClickHouse dictionaries, joining small tables to larger fact tables as necessary.

Choosing ORDER BY and PARTITION BY

  • The ORDER BY clause should leverage columns frequently appearing in WHERE, GROUP BY, or ORDER BY clauses.
  • PARTITION BY typically follows time-based criteria. Avoid excessive partitioning to minimize overhead and enhance efficiency.

Materialized Views and Pre-Aggregations

  • Materialized views can populate aggregated tables in real-time, ideal for quick-access dashboards.
  • Pre-aggregate common GROUP BY queries and maintain rollup tables.

TTL and Data Lifecycle Management

  • Use TTL expressions to automate deletion or relocation of outdated data, aiding retention policies.

Slowly Changing Dimensions and Joins

  • For slowly changing dimensions, adopt periodic snapshot tables or small dimension tables joined dynamically during queries.
  • Employ JOIN methods with small broadcasted tables or ClickHouse dictionaries for improved lookups.

Querying and Performance Tips

SQL Best Practices

  • Streamline queries by pushing filters that align with the ORDER BY and PARTITION BY clauses to allow for effective range reads.
  • LIMIT BY is recommended for top-N selections per group: it’s both fast and memory-efficient.
  • Employ SAMPLE for exploratory queries when exact results are not necessary.

Avoiding Expensive Joins

  • Refrain from large-to-large joins whenever possible. Instead, pre-join or denormalize data during ingestion.
  • For essential joins, ensure the smaller table is minimal or utilize dictionary tables.

Aggregate and Approximate Functions

  • Use approximate functions like uniqHLL for cardinality computations, which are significantly more efficient than exact counts.
  • Leverage array and higher-order functions for event analytics.

Vectorized Execution and Parallelism

  • ClickHouse utilizes vectorized execution, enabling operation across CPU cores for optimal performance. Adjust max_threads and max_memory_usage as required.

Schema & Index Tuning

  • The choice of ORDER BY is pivotal: align it closely with common query predicates.
  • The SAMPLE BY clause supports fast approximate results for extensive datasets.

Compression Comparison (Quick Table)

CodecCPU CostCompression RatioWhen to Use
LZ4LowModerateLow-latency queries, minimal CPU overhead
ZSTDMedium-HighHighCost-sensitive storage, large cold datasets
Delta / GorillaLow-MediumHighTime-series with monotonic or similar data

Ingestion and ETL Patterns

Batch vs. Streaming

  • Batch: Prioritize large bulk inserts for maximum efficiency. Utilize substantial INSERT commands or files to optimize part creation.
  • Streaming: Take advantage of Kafka integration for near-real-time data ingestion.

Kafka Integration

  • The Kafka table engine, in conjunction with materialized views, allows ClickHouse to consume Kafka data streams and insert them into MergeTree tables.
  • This strategy can support exactly-once or at-least-once delivery based on your producer/consumer setup and offset management.

Bulk Loading and S3

  • Use INSERT commands for batched CSV/TSV/JSON loads, or leverage S3 integration for data loading/exporting, a great option for backups and cold storage.

Data Validation and Deduplication

  • Ensure ingestion processes are idempotent; incorporate unique event IDs to facilitate deduplication in ReplacingMergeTree or similar variants.

Monitoring, Backup, and Operations

Replication, Shards, and High Availability

  • Implement ReplicatedMergeTree engines for replication with ZooKeeper or ClickHouse Keeper.
  • A standard production cluster typically features multiple replicas per shard to enable failover.

Backups

  • Backups can be logical (queries resulting in CSV/TSV exports) or physical (copying data parts to S3). Consider snapshotting parts for expedited recovery.

Key Metrics to Monitor

  • Query latency and active query counts.
  • The size of the merge queue and the number of parts (check system.parts).
  • Memory usage per query (queries that exceed available memory will be terminated).
  • Disk utilization and replication lag.

Scaling and Hardware Recommendations

  • ClickHouse performs best with fast disks (NVMe) and strong CPUs for decompression and vectorized execution, alongside adequate RAM.
  • For storage decisions and RAID configurations, consult Storage/RAID Configuration Guide.
  • If experimenting on-premise or in a home lab, refer to our NAS Guide.

Common Pitfalls and Troubleshooting for Beginners

  • Running out of memory during complex queries: adjust max_memory_usage and test with sampled data.
  • Issues with poor partitioning or sort key selection may result in inefficient queries that scan unnecessary data.
  • Excessive small parts resulting from single-row inserts can be mitigated by implementing batch inserts.
  • Misunderstanding JOIN operations and uniqueness constraints: remember that ORDER BY is not a uniqueness constraint.

Useful System Tables for Diagnostics

-- Active processes
SELECT * FROM system.processes;
-- Status of parts / merges
SELECT * FROM system.parts WHERE table = 'events';
-- Query log
SELECT * FROM system.query_log ORDER BY event_time DESC LIMIT 10;

For advanced troubleshooting guides and real-world case studies, be sure to check Altinity’s blog.

A Simple Hands-On Example (Mini Tutorial)

In this section, we will create a basic event table, perform batch inserts, run queries, and establish a materialized view for pre-aggregations within a local Docker instance.

Step 1: Create an Events Table

CREATE TABLE IF NOT EXISTS events (
  event_date Date,
  event_ts DateTime,
  user_id UInt64,
  session_id String,
  page String,
  event_type String,
  properties String
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_date)
ORDER BY (event_date, user_id, event_ts)
SETTINGS index_granularity = 8192;

Step 2: Insert Batched Sample Data

INSERT INTO events FORMAT CSV
2025-05-02,2025-05-02 10:00:01,1234,session_1,"/home","page_view","{}"
2025-05-02,2025-05-02 10:00:05,1235,session_2,"/pricing","click","{}"

Typical Analytics Queries

  • Daily Active Users (DAU):
SELECT event_date, count(DISTINCT user_id) AS dau
FROM events
WHERE event_date BETWEEN '2025-05-01' AND '2025-05-07'
GROUP BY event_date
ORDER BY event_date;
  • Top Pages:
SELECT page, count() AS views
FROM events
WHERE event_date = '2025-05-02'
GROUP BY page
ORDER BY views DESC
LIMIT 20;
  • Time Series Aggregation by Hour:
SELECT toStartOfHour(event_ts) AS hour, count() AS events
FROM events
WHERE event_date = '2025-05-02'
GROUP BY hour
ORDER BY hour;

Step 3: Create a Materialized View for Daily Aggregates

CREATE TABLE IF NOT EXISTS events_agg_daily (
  event_date Date,
  page String,
  views UInt64
) ENGINE = MergeTree()
ORDER BY (event_date, page);

CREATE MATERIALIZED VIEW IF NOT EXISTS mv_events_agg_daily
TO events_agg_daily
AS
SELECT
  event_date,
  page,
  count() AS views
FROM events
GROUP BY event_date, page;

This materialized view will automatically populate the events_agg_daily table, providing fast access to daily aggregate data suitable for dashboards.

Conclusion and Next Steps

ClickHouse stands out as an exceptional solution for rapid, large-scale analytics. It guarantees impressive query performance, economical storage through effective compression methods, and remarkable scalability when properly configured. Key takeaways for beginners include:

  • Design data for analytical accessibility by favoring denormalized wide tables and selectively establishing ORDER BY and PARTITION BY clauses.
  • Optimize data ingestion processes using batch inserts and adopt materialized views for recurring aggregations.
  • Monitor the system continuously for merges, memory usage, and disk performance; use ReplicatedMergeTree for high availability.

Suggested Next Steps and Practice Projects:

  • Launch a local ClickHouse Docker instance and import a website event log to create DAU and funnel dashboards.
  • Integrate ClickHouse with Grafana for real-time dashboarding.
  • Experiment with streaming ingestion from Kafka into ClickHouse while measuring end-to-end latency.
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