Results for “partitioning”
12 skills
wondelai ddia-systems
Design reliable, scalable, and maintainable data systems by applying principles from storage engines, replication, partitioning, transactions, and consistency models.
1.6k · bundle
claude-dev-suite oracle
Oracle Database specific features. Covers data types, sequences, synonyms, partitioning, and Oracle-specific SQL syntax. Use for Oracle database work. USE WHEN: user mentions "oracle", "oracle database", "sequences", "synonyms", "DUAL", "SYSDATE", "NVL", "DECODE", "Oracle partitioning", "Oracle specifics" DO NOT USE FOR: PostgreSQL - use `postgresql` instead, SQL Server - use `sqlserver` instead, PL/SQL programming - use `plsql` instead
28 · bundle
More results
github qdrant-tenant-scaling
Guides scaling Qdrant for multi-tenant workloads using payload partitioning, custom sharding, and tiered multitenancy.
36.2k
github qdrant-horizontal-scaling
Diagnoses Qdrant capacity needs and guides horizontal scaling decisions, including node count, shard count, replication factor, and resharding trade-offs.
36.2k
github qdrant-scaling-query-volume
Optimizes Qdrant query performance for large limits across multiple shards by using Poisson-distributed subsampling to reduce inter-shard data transfer.
36.2k
lingxling dask
Scales pandas and NumPy workflows to datasets larger than memory using parallel and distributed computing, with support for dataframes, arrays, bags, and custom task graphs.
253 · bundle
artubss dask
Computação paralela/distribuída. Escale pandas/NumPy além da memória disponível, DataFrames/Arrays paralelos, processamento multi-arquivo, grafos de tarefas, para datasets maiores que RAM e workflows paralelos.
10 · bundle
levalencia dask
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
3 · bundle
tools-only 162-use-e70e84f3
Provides guidance on using Apache Spark RDDs, including creation, transformations, actions, and performance considerations.
7 · bundle
timlai666 dask
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
1 · bundle
theheavenlyd3mon ddia-systems
Design data systems by understanding storage engines, replication, partitioning, transactions, and consistency models. Use when the user mentions "database choice", "replication lag", "partitioning strategy", "consistency vs availability", "stream processing", "ACID transactions", "eventual consistency", or "LSM tree vs B-tree". Also trigger when choosing between SQL and NoSQL, designing data pipelines, or debugging distributed system consistency issues. Covers data models, batch/stream processing, and distributed consensus. For system design, see system-design. For resilience, see release-it.
28 · bundle
nvidia cupynumeric-parallel-data-load
Load sharded datasets (npy, Parquet, HDF5, raw binary) into distributed cuPyNumeric arrays using manual partitioning and Legate task launches.
2.2k · bundle