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What are the advantages and challenges of using hash partitioning for large fact tables?

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Hash partitioning is a common technique for distributing large fact tables across multiple physical partitions in a data warehouse. It can improve query performance, scalability, and manageability, but it also comes with some trade-offs and challenges. In this article, you will learn what hash partitioning is, how it works, and what are its advantages and challenges for dimensional modeling.

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1 What is hash partitioning?

Hash partitioning is a method of dividing a table into smaller and more manageable pieces based on a hash function. A hash function is a mathematical formula that takes a value as input and produces a fixed-length output, called a hash or a bucket. For example, a simple hash function could be to take the last digit of a number and assign it to a bucket from 0 to 9. Hash partitioning applies the hash function to a column or a combination of columns in the table, called the partition key, and assigns each row to a partition based on the hash value.

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Sachin Hawaldar

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Hash partitioning basically used for managing the physical distribution of data for improved performance & scalability. Primary purpose for Hash partitioning is to evenly distribute data for load balancing, efficient parallel processing, and optimal use of system resources. Imagine you have a big box of candies, and you want to organize them in a way that makes it easy to find your favorite flavors quickly. You decide to use different colored buckets to group similar candies together. Now, the question is, how do you decide which bucket a candy goes into? - 1) Choosing bucket using magical color-sorting technique (the hash function 2) making sure it is evenly distributed - and 3) finding candy can be easy.

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2 How does hash partitioning work?

Hash partitioning works by creating a number of partitions equal to the number of distinct hash values. For example, if the hash function produces 10 buckets, then the table will be split into 10 partitions. Each partition is stored in a separate physical location, such as a disk or a node in a cluster. The data warehouse can then use the hash function to locate the partition that contains the data for a given query. For example, if the query filters on the partition key, the data warehouse can compute the hash value and access only the relevant partition, instead of scanning the entire table.

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3 What are the advantages of hash partitioning?

Hash partitioning has several advantages for large fact tables in a dimensional model, such as improving query performance by reducing the amount of data that needs to be scanned, filtered, or joined. This can also save disk space and memory usage. Additionally, it can improve scalability by distributing the data and workload evenly across multiple partitions and nodes, thus enhancing parallelism and concurrency. Furthermore, it can improve manageability by allowing the data warehouse to perform operations on individual partitions instead of on the whole table, reducing the impact of locking and contention.

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Florin Badita

Data Mining for Startups (Series A-C) | +10k websites scrapped, collected billion of data points, +10 yrs exp | Do you need data that is difficult to find? Contact me - Forbes U30 / Tedx Speaker
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Improved Parallelism: When querying a hash-partitioned table, the database can potentially execute queries in parallel across multiple partitions, taking advantage of multi-core CPUs and I/O concurrency. This can result in faster query performance compared to a single large table. Reduced I/O: Hash partitioning can reduce the amount of I/O required for queries because each query only accesses a subset of the data in the appropriate partition. This can lead to more efficient use of disk I/O and faster query execution. Efficient Pruning: Hash partitioning can efficiently prune partitions that do not contain relevant data for a query. This means that the database can skip scanning entire partitions, further improving query performance.

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4 What are the challenges of hash partitioning?

Hash partitioning for large fact tables in a dimensional model can present some challenges and limitations. It can be difficult to choose an appropriate hash function and partition key that ensure good balance of data and performance, and this can result in skewed or uneven partitions which can affect query performance and scalability. Changing or modifying the hash function or the partition key once the table is partitioned can be difficult, and require re-partitioning or re-distributing the data, which can be costly and time-consuming. Additionally, queries that involve multiple partitions or non-partitioned columns can be difficult to perform, resulting in scanning or joining multiple partitions which reduces query performance and increases network traffic.

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Sachin Hawaldar

Analytics |Problem Solver | Data Storyteller| Coach & Mentor|
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a) Deciding what columns to use for partitioning to make sure it is evenly distributed? b) Removing and adding partition is resource intensive. c) Hash Collisions- If values from the columns are produced are different that expected. d) Maintenance overhead

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Florin Badita

Data Mining for Startups (Series A-C) | +10k websites scrapped, collected billion of data points, +10 yrs exp | Do you need data that is difficult to find? Contact me - Forbes U30 / Tedx Speaker
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Maintenance Overhead: Partitioning adds some maintenance overhead, as you need to manage multiple partitions. Regularly monitoring and maintaining partitions is important to ensure continued performance benefits.

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5 How to use hash partitioning effectively?

Hash partitioning is an effective technique for large fact tables in a dimensional model, but it requires careful planning and testing. To use it effectively, choose a hash function and partition key that are consistent, deterministic, and uniform. The partition key should have a high cardinality and low correlation with other columns. Additionally, the number of partitions should match the number of nodes or disks in your data warehouse to avoid data skew and optimize resource utilization. Furthermore, align the partition key with your query patterns and filters to reduce the number of partitions accessed or joined for a given query. Finally, monitor and evaluate the performance and distribution of your partitions regularly to identify and resolve any issues or bottlenecks.

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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?

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Florin Badita

Data Mining for Startups (Series A-C) | +10k websites scrapped, collected billion of data points, +10 yrs exp | Do you need data that is difficult to find? Contact me - Forbes U30 / Tedx Speaker
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There is no option for automatically creating matching indexes on all partitions. Separate commands must be used to add indexes to each partition. There is no way to build a primary key, a unique constraint, or an exclusion constraint that spans all partitions; instead, each leaf partition must be constrained separately. Foreign keys referencing partitioned tables, as well as foreign key references from a partitioned table to another table, are not supported because primary keys are not supported on partitioned tables. It is not possible to mix temporary and permanent relations in the same partition tree.

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What are the advantages and challenges of using hash partitioning for large fact tables?

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1 What is hash partitioning?

2 How does hash partitioning work?

3 What are the advantages of hash partitioning?

4 What are the challenges of hash partitioning?

5 How to use hash partitioning effectively?

6 Here’s what else to consider

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