Observed Signal · May 1, 2026 · Technical Guide · Source: DEV Community · Impact: 3/5 · Sentiment: Positive
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.
Provides a current, production-grade ClickHouse installation and optimization blueprint for modern Ubuntu and bare-metal servers — relevant to teams building high-throughput analytics and vector search infrastructure.
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Key Takeaways & Evidence Grounding
- Guide demonstrates installing ClickHouse on Ubuntu 26.04 using the modern GPG keyring method (replacing apt-key).
- Recommends a tiered storage policy mixing NVMe (hot) and HDD (cold) with automatic movement of merged parts larger than 10GB.
- Advises enabling async_insert to batch small writes in memory and prevent the 'Too many parts' error.
- Documents ClickHouse Vector Search via HNSW indexes and recommends high-frequency Intel Xeon or AMD EPYC CPUs (SIMD/AVX-512) for query performance; GPUs only for embedding generation.
- Suggests replacing Apache ZooKeeper with ClickHouse Keeper and shows installing clickhouse-keeper for distributed clusters.
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Deploying SigNoz with ClickHouse v25 and OTel Gotchas
A developer describes troubleshooting and deployment patterns for self-hosting SigNoz in 2026, focusing on ClickHouse v25 configuration changes, OpenTelemetry Collector incompatibilities, and Docker network isolation. Key recommendations include using config.d/users.d for ClickHouse overrides (instead of replacing the main config), enforcing a deterministic docker-compose boot sequence with one-shot migrator containers (signoz_init_clickhouse and signoz_telemetrystore_migrator), updating exporter names and image tags for the OTel collector (use signozclickhousemetrics and include the 'v' prefix), and relying on internal Docker network isolation rather than per-component passwords to simplify connectivity. The post emphasizes that maintaining self-hosted observability infrastructure requires ongoing engineering effort.
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.
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.
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