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Arpit Bhayani Blogs

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Arpit Bhayani Blogs
001 Ai Topological Sort

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TLDR: Partitioning Data - Range, Hash, and When to Use Them

Date: 2022-01-31 Source: https://arpitbhayani.me/blogs/some-data-partitioning-strategies-for-distributed-data-stores

Overview

Partitioning - Learn how to scale your database reads and writes by horizontally partitioning your data. Explore range-based vs hash-based approaches. Partitioning plays a vital role in scaling a database beyond a certain scale of reads and writes.

Key Points

  • Partitioning plays a vital role in scaling a database beyond a certain scale of reads and writes.
  • Our goal with partitioning: Our primary goal with partitioning is to spread the data across multiple nodes, each responsible for only a fraction of the data allowing us to dodge the limitations with vertical scaling.
  • What if partitioning is skewed?: Partitioning does help in handling the scale only when the load spreads uniformly.
  • Range-based Partitioning: One of the most popular ways of partitioning data is by assigning a continuous range of data to each partition, making each partition responsible for the assigned fragment.
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003 Rag Production
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004 Structure Of Llm Chat
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105 Data Partitioning
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  • Hash-based Partitioning: Another popular approach for horizontal partitioning is by hashing the partitioned attribute and determining the partition that will own the record.

    • Takeaway

    Another popular approach for horizontal partitioning is by hashing the partitioned attribute and determining the partition that will own the record.