Observed Signal · Jun 19, 2026 · Technical Tutorial · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
ClickHouse HTTP API: Guide to Queries and Ingestion
This technical tutorial explains ClickHouse's built-in HTTP API, a language‑agnostic interface that accepts SQL over HTTP (default port 8123) using GET or POST. The article covers verifying the API (ping), executing queries via GET parameters or POST bodies, authentication options (URL parameters, HTTP Basic, and recommended HTTP headers), selecting databases, supported output formats (JSON, JSONEachRow, CSV, TSV, Parquet, Arrow, TabSeparated), inserting data in multiple formats, and performing DDL/DML over HTTP. It also lists useful HTTP parameters and best practices (use POST for long queries, prefer headers for credentials, enable compression, set max_execution_time). The guide positions the HTTP API as convenient for scripting, automation, REST integrations and lightweight services.
Practical guide to ClickHouse HTTP API aids integrations, ETL and analytics workflows commonly used in data pipelines; useful for engineering teams but not industry‑shifting.
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Key Takeaways & Evidence Grounding
- ClickHouse exposes a built-in HTTP API that runs by default on port 8123 and accepts GET and POST requests.
- The HTTP API accepts SQL queries in the request and returns results in multiple formats including JSON, JSONEachRow, CSV, TSV, Parquet, Apache Arrow and TabSeparated.
- Authentication methods supported for the HTTP API include URL parameters, HTTP Basic Auth, and request headers (X-ClickHouse-User, X-ClickHouse-Key); headers are recommended.
- The HTTP API supports data ingestion (INSERT) in formats such as TabSeparated, CSV and JSONEachRow, and supports standard DDL operations (CREATE, DROP, ALTER).
- Useful HTTP parameters include query, user, password, database, default_format, max_rows_to_read, max_execution_time and compress to control execution and responses.
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ClickHouse JSON: Choosing the Right Storage Approach
A DEV Community technical post (published 2026-06-17) explains best practices for storing and querying JSON in ClickHouse. The author contrasts storing JSON as raw String with ClickHouse’s native JSON data type, describing trade-offs between ingestion flexibility and query performance. ClickHouse’s native JSON offers lazy parsing so only referenced fields are parsed at query time, improving efficiency on semi-structured workloads. The article recommends schema design driven by query patterns: frequently queried fields (user_id, event_type, timestamp) should be modelled as dedicated columns, while less-accessed or volatile metadata can remain in a JSON column. The post advocates a hybrid approach to balance fast analytics, flexible schemas, and simpler ingestion pipelines.
Install and Optimize ClickHouse on Ubuntu 26.04
A 2026 technical guide that shows how to install and optimize ClickHouse on Ubuntu 26.04 (also applicable to 24.04 and 22.04) for production bare-metal deployments. The article covers a secure modern repository setup using a GPG keyring, a tiered storage policy that routes active workloads to NVMe and archives large parts to HDD, network binding and firewall guidance, fixes for the 'Too many parts' issue via async inserts, ClickHouse's vector search (HNSW) recommendations, and replacing Apache ZooKeeper with ClickHouse Keeper. It also recommends strict memory quotas to prevent OOM and hardware choices (high-frequency CPUs) for CPU-bound vector search workloads.
ClickHouse vs PostgreSQL: OLAP vs OLTP Comparison
A developer-authored technical post comparing ClickHouse and PostgreSQL as part of a '100 Days of ClickHouse' series. The article explains that PostgreSQL is a row-oriented OLTP database optimized for transactional workloads with frequent inserts/updates/deletes and strong transactional guarantees, while ClickHouse is a column-oriented OLAP database designed for large-scale analytics, fast aggregations and time-series/event analytics. It outlines storage and compression differences, scaling considerations, and common deployment patterns where organizations use PostgreSQL for operational data and ClickHouse for analytical/reporting workloads. The piece aims to guide database selection based on workload requirements rather than popularity or benchmarks.
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