登录查看更多内容

How do you properly partition and shard data for distributed databases?

由人工智能和领英社区提供技术支持

Partitioning and sharding are two techniques for managing large and complex data sets in distributed databases. They can improve the performance, scalability, and availability of your data, but they also require careful planning and design. In this article, you will learn what partitioning and sharding are, how they differ, and how to apply them properly for your data science projects.

此文章中的业界达人

由社区从 4 条内容中精选。了解更多

Bharath Kumar Mittapally

Data Science Consultant | Mentor | Problem Solver
Ravish Kumar

Solutions Architect at Total eBiz Solutions | Data-driven solutions expert
Raghvendra Kumar

IT Industry | Data Analyst | Data Science | Machine Learning | Python | Tableau | PySpark

1 What is partitioning?

Partitioning is the process of dividing a large data set into smaller and more manageable subsets, called partitions. Each partition can be stored on a different node or server in a distributed database system, which reduces the load and latency of data access. Partitioning can also enable parallel processing, fault tolerance, and backup and recovery of data. However, partitioning also introduces some challenges, such as balancing the data distribution, maintaining data consistency, and optimizing data queries across partitions.

添加您的观点

Raghvendra Kumar

IT Industry | Data Analyst | Data Science | Machine Learning | Python | Tableau | PySpark
举报内容
Database partitioning refers to breaking the data in an application’s database into separate pieces, or partitions. These partitions can then be stored, accessed, and managed separately.

已翻译

赞

2 What is sharding?

Sharding is a specific type of partitioning, where each partition is independent and self-contained. This means that each partition has its own schema, index, and primary key, and does not share any data with other partitions. Sharding can increase the horizontal scalability of a distributed database system, as you can add more partitions and nodes without affecting the existing ones. Sharding can also improve the performance and availability of your data, as you can distribute the workload and avoid single points of failure. However, sharding also has some drawbacks, such as increasing the complexity of data operations, requiring more coordination and synchronization, and limiting the data analysis and aggregation capabilities.

添加您的观点

Ravish Kumar

Solutions Architect at Total eBiz Solutions | Data-driven solutions expert
举报内容
As a data engineer, I boosted analytics on a 50TB customer database. Segmentation queries took hours, slowing decisions. We optimized with indexes and aggregation tables, but hit a limit. I sharded the data to scale out the workload. I worked with data scientists to analyze queries and frequencies, then partitioned data by attributes. This routed queries to shards for parallel processing. Query times got faster! But we faced issues with cross-shard queries and reintegration. We solved them by caching cross-shard data and running MapReduce. Sharding was complex, but it scaled analytics for quick insights without losing integrity. The key was evaluating patterns before sharding smartly. With sharding, hours became minutes, a game changer!

已翻译

赞

3 How to partition data?

Partitioning data can be done in various ways, depending on the criteria and logic used to divide the data set. Range partitioning involves splitting the data based on a range of values, such as dates, numbers, or alphabets. For example, sales data can be partitioned by month, quarter, or year. List partitioning is when the data is divided by a predefined list of values, such as categories, regions, or customer types. Hash partitioning involves assigning a hash value to each data item using a hash function which then determines which partition the data item belongs to. Composite partitioning is a combination of two or more partitioning methods; for example, transaction data can be partitioned by range and hash using the date as the range and the transaction ID as the hash.

添加您的观点

Wendy Wong
举报内容
In my experience I partition data in a cloud data warehouse such as Amazon Redshift when I create the table. While ingesting data, you may retain the existing variable for date and also define virtual columns such as month and year. This allows you to query a portion of the dataset with the partitions month and year in using WHERE clause in SQL. Partitioned data makes your SQL query scans much faster and returns the results you need to answer your questions.

已翻译

赞

4 How to shard data?

When it comes to sharding data, there are several methods to choose from, depending on the key and strategy used to split the data set. Key-based sharding involves sharding the data based on a key that uniquely identifies each data item, such as a primary key or a foreign key. Lookup-based sharding requires using a lookup table that maps each data item to a shard. Alternatively, directory-based sharding relies on a directory service that dynamically assigns each data item to a shard. These services can be centralized or distributed systems that maintain the metadata and the location of each shard. For example, you can use any of these methods to shard customer data sets by customer ID, order data sets by order ID, product data sets by product ID, and document data sets by document name.

添加您的观点

5 How to choose the right partitioning and sharding method?

Partitioning and sharding data is a complex task, as there is no one-size-fits-all solution. To choose the best method, you need to consider factors such as the size and growth rate of your data set, the access patterns and query requirements, the performance and scalability goals of your system, and the trade-offs between data consistency, availability, and partition tolerance. Testing and evaluating different partitioning and sharding methods is essential. Tools like Hadoop, Spark, MongoDB, and Cassandra can help you implement and manage partitioning and sharding. As a data scientist working with large and complex data sets in distributed databases, you must understand the concepts of partitioning and sharding to make informed decisions for your data projects. While they can help optimize data quality and integrity as well as analysis and processing, they require careful planning, design, monitoring, and adjustment.

添加您的观点

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Bharath Kumar Mittapally

Data Science Consultant | Mentor | Problem Solver
举报内容
When organizing your data, balance automation with customization. Some systems automatically partition data, but look at your unique data patterns to guide more personalized strategies. For example, frequently accessed data subsets may deserve dedicated partitions. However, while sharding expands capacity, it also introduces complex coordination. Ensure disparate data integrates smoothly. Above all, prioritize the end-user experience. As you modify your infrastructure, consider how it impacts data speed, accessibility and user interaction. Monitor and adjust regularly to optimize for a streamlined, user-centric system.

已翻译

赞

Data Science

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

How do you properly partition and shard data for distributed databases?

1

2

3

4

5

6

1 What is partitioning?

2 What is sharding?

3 How to partition data?

4 How to shard data?

5 How to choose the right partitioning and sharding method?

6 Here’s what else to consider

Data Science

给文章评分

感谢您的反馈

更多Data Science相关文章

更多相关阅读内容

How do you properly partition and shard data for distributed databases?

1

2

3

4

5

6

1 What is partitioning?

2 What is sharding?

3 How to partition data?

4 How to shard data?

5 How to choose the right partitioning and sharding method?

6 Here’s what else to consider

Data Science

给文章评分

感谢您的反馈

查看其他技能