ytsaurus-client
Python client for YTsaurus system and miscellaneous libraries.
Decision gist · record as of 2026-08-14
Yes, if you have access to a YTsaurus cluster and need to interact with it from Python. The package is actively maintained, has low installation friction, carries no known vulnerabilities, and uses a permissive license. The main prerequisite is having a YTsaurus deployment available; without one, the client alone is not useful.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires access to a running YTsaurus cluster; the client alone cannot function without a deployment to connect to.
- Low installation friction with a pure-wheel distribution and seven common runtime dependencies.
- Active maintenance with a release 21 days old.
License · maintenance · safety
Apache 2.0 (permissive) — Licensed under Apache 2.0 (permissive), allowing free use, modification, and distribution with minimal restrictions.
last release 2026-07-24 (21 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 758,158 downloads/mo, #5,134 on PyPI
Alternatives
Verify before relying
pip install ytsaurus-client
import ytsaurus_client
# Connect to cluster and perform operations- Whether the package requires a YTsaurus cluster to be running or accessible to function
- Documentation availability and completeness for getting started with the client
- Community adoption and real-world usage patterns beyond download metrics
What it is and what it does
ytsaurus-client is a Python wrapper around the YTsaurus distributed computing platform, which combines a distributed file system, MapReduce engine, and NoSQL storage. The library abstracts the complexity of interacting with a YTsaurus cluster, letting you submit jobs, read and write data, and manage operations from Python code.
It depends on standard libraries like simplejson for data serialization, tqdm for progress tracking, and charset-normalizer for text handling. The package requires Python 3.8 or later and is actively maintained, with its most recent release from July 2026.
Use it for
- Submit and monitor MapReduce jobs to a YTsaurus cluster from Python scripts
- Read and write large datasets stored in a YTsaurus distributed file system
- Query and manage key-value data in YTsaurus NoSQL storage from application code
- Automate data pipeline orchestration using YTsaurus as the compute backend
- Integrate YTsaurus operations into Python-based data processing workflows
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have access to a YTsaurus cluster and need to interact with it from Python.
The package is actively maintained, has low installation friction, carries no known vulnerabilities, and uses a permissive license. The main prerequisite is having a YTsaurus deployment available; without one, the client alone is not useful.
Install
ytsaurus-client on PyPI
Before you install
Low installation friction with a pure-wheel distribution and seven common runtime dependencies. Active maintenance with a release 21 days old.
Requires access to a running YTsaurus cluster; the client alone cannot function without a deployment to connect to.
License in practice
Licensed under Apache 2.0 (permissive), allowing free use, modification, and distribution with minimal restrictions.
Quickstart
pip install ytsaurus-client
import ytsaurus_client
# Connect to cluster and perform operations
Verify before relying
- Whether the package requires a YTsaurus cluster to be running or accessible to function
- Documentation availability and completeness for getting started with the client
- Community adoption and real-world usage patterns beyond download metrics
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagessimplejsondecoratortqdmargcompletecharset-normalizertyping-extensionsdistro |
| Maintenance | Actively maintained 21 days since the last release |
| First released | |
| Downloads | 758,158 / month, #5,134 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: ytsaurus_client-0.13.53-py2.py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “ytsaurus python client”
- ytsaurus-clientProvides a Python client library for YTsaurus, enabling programmatic…
- ytsaurus-ysonProvides C++ bindings for serializing and deserializing YSON format…
- codewords-clientA Python client library for the Codewords API with built-in FastAPI…
Give your agent the search over MCP, or paste the wish link into any chat.
More Distributed Computing packages
gRPC Python is an HTTP/2-based RPC framework that enables you to define and call remote procedures across network boundaries using protocol buffers for serialization.
Install it if you need RPC communication in a distributed system or are integrating with existing gRPC services.
execnet lets you spawn and communicate with Python interpreters across local processes, remote hosts, and different platforms, using a simple API for task distribution and inter-process messaging.
However, the aging maintenance status (275 days since last release) means you should verify it meets your concurrency and performance needs before committing to a…
Cloudpickle extends Python's standard pickle module to serialize lambda functions, interactively-defined functions and classes, and other constructs that the default pickle cannot handle, making it suitable for cluster computing and remote code execution.
Install it if you need to serialize lambda functions, interactively-defined code, or non-standard Python constructs for cluster computing or distributed execution.
Provides a unified, open()-compatible Python API for streaming large files from remote storage (S3, GCS, Azure, HDFS, SFTP, HTTP) and local filesystems, with transparent compression support.
Install it if you work with large files on cloud storage or remote systems and want to avoid writing boilerplate around multiple SDKs.
Portalocker provides cross-platform file locking with support for exclusive and shared locks, plus Redis-based distributed locks and process-aware PID file locking.
Install it if you need file or process coordination; the optional extras (pywin32, redis) are only required for specific lock types.
Ray is a distributed computing framework that scales Python applications from a single machine to multi-node clusters, providing abstractions for parallel tasks, stateful actors, and shared objects.
See also ytsaurus-yson · hazelcast-python-client · aerospike · pyspark-client · aioredis · kr8s · google-cloud-datastore · pymetastore · drjax · google-cloud-firestore