WClickHouse Library Loads 100,000-Row Pandas DataFrames into ClickHouse in Under 0.6s

·5 views

A Python library called WClickHouse has introduced an insert_dataframe() method designed to load Pandas DataFrames into ClickHouse analytical databases at near-network speed, eliminating the need for slow row-by-row iteration. The tool addresses common pain points for data scientists, including type incompatibilities between Pandas float64/NaN values and ClickHouse's nullable types, as well as Jupyter Notebook crashes when exporting large datasets. According to the developers, the method can ingest 100,000 rows in under 0.6 seconds using a vectorized backend built on Apache Arrow and Pydantic v2. The library supports Python versions 3.9 through 3.14 and claims over 95% test coverage against real ClickHouse instances. WClickHouse is available on both GitHub and PyPI as an open-source project.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Developer shares structured workflow for building and releasing open source projects

A software developer has outlined a practical, step-by-step approach to taking coding ideas from concept to publicly released open source projects. The workflow prioritises identifying a real problem before choosing a technology, then progresses through requirements, a minimal working version, testing, and documentation before any release. The developer emphasises that good documentation — including a clear README, CONTRIBUTING, and CHANGELOG files — is as important as the code itself for attracting contributors. Automated testing and descriptive Git commit messages are highlighted as key habits that keep projects maintainable over time. The post argues that publishing code and iterating based on real-world feedback provides learning that tutorials alone cannot replicate.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Builds Clash of Clans War Outcome Simulator Using Official Game API

A developer discovered the Clash of Clans public API and used it to build a war simulation tool called WarOracle. The tool analyzes a clan's current war data, including player Town Hall levels, heroes, equipment, and past attack history, to estimate how individual attacks might perform. It then runs Monte Carlo simulations across remaining war attacks to produce win, loss, or draw probabilities along with projected star counts and score ranges. For ongoing wars, already-completed attacks are treated as fixed outcomes, with only the remaining attacks being simulated. The backend is built with Spring Boot and Java, while the frontend uses React, and the developer is still evaluating whether the tool offers genuine value to active Clash of Clans players.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Independent Benchmark Pits TypeSafe's Jev Model Against GPT-4, Claude, and Gemini

A developer has published an independent benchmark evaluating TypeSafe's Jev model against leading large language models including GPT-4, Claude, and Gemini. Unlike conventional LLMs, Jev outputs probabilities for predefined answer choices rather than generating free-form text. The benchmark focused on classification tasks such as spam detection, sentiment analysis, and topic classification. The tests were designed with reproducibility in mind, with full code made available via a public GitHub repository. Jev's probability-based approach is seen as potentially useful for intent routing, safety guardrails, and low-latency classification pipelines in AI systems.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Why micro frontend contracts matter more than bundler config

A development team building a React-based government events platform adopted Module Federation to allow independent deployments across multiple teams, replacing a monolithic release process that forced everyone to wait on the slowest team. The core challenge they identified was not the bundler setup, which can be configured in hours, but defining and enforcing explicit contracts between the shell and remote modules. Each remote was required to declare its exposed props, emitted events, and shared dependency version ranges, with the shell refusing to mount any remote that fell outside the agreed range. This approach moved failures to load time rather than production, eliminated release-train coordination, and reduced the overall release cycle by roughly 40 percent. The team concluded that splitting frontends by team ownership, keeping shared dependencies minimal, and writing the contract before touching build config are the critical factors for sustainable micro frontend architecture.

0 comments Read more at DEV Community