Observed Signal · Jun 5, 2026 · Publication · Source: DEV Community · Impact: 2/5 · Sentiment: Neutral
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.
Provides practical guidance on choosing between analytical (ClickHouse) and transactional (PostgreSQL) databases; relevant to architects and data engineers designing data pipelines and analytics stacks.
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Key Takeaways & Evidence Grounding
- Published on 2026-06-05 by Kanishga Subramani on DEV Community.
- The post compares PostgreSQL (OLTP, row-oriented) and ClickHouse (OLAP, column-oriented).
- PostgreSQL is recommended for transactional workloads requiring frequent inserts/updates/deletes and strong transactional guarantees.
- ClickHouse is recommended for real-time analytics, large-scale reporting, time-series processing, and fast aggregations across billions of rows.
- Many organizations deploy both: PostgreSQL for operational data and ClickHouse for analytical/reporting data.
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Open-source apitap moves 10M rows in 9.9s
The author published apitap, an open-source Rust-core with Python bindings engine that copies whole tables between databases with no config. Using the pip-distributed wheel on a 16-core machine, apitap moved 10 million rows from Postgres to ClickHouse in 9.9 seconds and sustained 100M rows / 46 GB in 139 seconds (1.2 TB/hour) in later runs. apitap supports Postgres→Postgres, Postgres→ClickHouse, and MySQL→ClickHouse/Postgres routes, uses bounded streaming memory to run in very small containers (0.5 vCPU / 256 MB), stages into shadow tables for atomic swaps, and includes a reproducible benchmark comparing it to dlt and ingestr. The project is MIT-licensed and open for contributors; additional connectors and platforms are on the roadmap.
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.
OLAP and OLTP Lines Are Blurring
A developer article explains how recent extensions and engine architectures are narrowing the gap between OLTP (transactional) and OLAP (analytical) workloads. It describes how extensions such as pg_lake decouple storage to cloud data lakes using Apache Iceberg while offloading analytical execution to an isolated, vectorized DuckDB process to avoid impacting the operational database. The author maps end-to-end execution flow, resource safety boundaries, and scheduling differences between macro-distributed query engines and micro-morsel (embedded/vectorized) processing engines. The post links to a detailed GitHub DeepDiveDuckDB repository for a full architecture layout. The piece is a technical analysis aimed at data engineers and platform architects.
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