- Understand the meaning of partitioning and bucketing in the Hive in detail.
- We will see, how to create partitions and buckets in the Hive
You might have seen an encyclopedia in your school or college library. It is a set of books that will give you information about almost anything. Do you know what is the best thing about the encyclopedia?
Yes, you guessed it correctly. The words are arranged alphabetically. For example, you have a word in mind “Pyramids”. You will directly go and pick up the book with the title “P”. You don’t have to search that in other books. Can you imagine how tough would the task be to search for a single book if they were stored without any order?
Here storing the words alphabetically represents indexing, but using a different location for the words that start from the same character is known as bucketing.
Similar kinds of storage techniques like partitioning and bucketing are there in Apache Hive so that we can get faster results for the search queries. In this article, we will see what is partitioning and bucketing, and when to use which one?
Table of Contents
- What is Partitioning?
- When to use Partitioning?
- What is Bucketing?
- When to use Bucketing?
What is Partitioning?
Apache Hive allows us to organize the table into multiple partitions where we can group the same kind of data together. It is used for distributing the load horizontally. Let’s understand it with an example:
Suppose we have to create a table in the hive which contains the product details for a fashion e-commerce company. It has the following columns:
Now, the first filter that most of the customer uses is Gender then they select categories like Shirt, its size, and color. Let’s see how to create the partitions for this example.
CREATE TABLE products ( product_id string, brand string, size string, discount float, price float ) PARTITIONED BY (gender string, category string, color string);
Now, the hive will store the data in the directory structure like:
Partitioning the data gives us performance benefits and also helps us in organizing the data. Now, let’s see when to use the partitioning in the hive.
When to use Partitioning?
- When the column with a high search query has low cardinality. For example, if you create a partition by the country name then a maximum of 195 partitions will be made and these number of directories are manageable by the hive.
- On the other hand, do not create partitions on the columns with very high cardinality. For example- product IDs, timestamp, and price because will create millions of directories which will be impossible for the hive to manage.
- It is effective when the data volume in each partition is not very high. For example, if you have the airline data and you want to calculate the total number of flights in a day. In that case, the result will take more time to calculate over the partition “Dubai” as it has one of the busiest airports in the world whereas for the country like “Albania” will return results quicker.
What is Bucketing?
In the above example, we know that we cannot create a partition over the column price because its data type is float and there is an infinite number of unique prices are possible.
Hive will have to generate a separate directory for each of the unique prices and it would be very difficult for the hive to manage these. Instead of this, we can manually define the number of buckets we want for such columns.
In bucketing, the partitions can be subdivided into buckets based on the hash function of a column. It gives extra structure to the data which can be used for more efficient queries.
CREATE TABLE products ( product_id string, brand string, size string, discount float, price float ) PARTITIONED BY (gender string, category string, color string) CLUSTERED BY (price) INTO 50 BUCKETS;
Now, only 50 buckets will be created no matter how many unique values are there in the price column. For example, in the first bucket, all the products with a price [ 0 – 500 ] will go, and in the next bucket products with a price [ 500 – 200 ] and so on.
When to use Bucketing?
- We cannot do partitioning on a column with very high cardinality. Too many partitions will result in multiple Hadoop files which will increase the load on the same node as it has to carry the metadata of each of the partitions.
- If some map-side joins are involved in your queries, then bucketed tables are a good option. Map side join is a process where two tables are joins using the map function only without any reduced function. I would recommed you to go through this article for more understanding about map-side joins: Map Side Joins in Hive
In this article, we have seen what is partitioning and bucketing, how to create them, and are pros and cons of them.
I would highly recommend you go through the following resources to learn more about Apache Hive:
- Getting Started with Apache Hive – A Must Know Tool For all Big Data and Data Engineering Professionals
- Types of Tables in Apache Hive – A Quick Overview
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