What is the use of bucketing in spark?
Bucketing is a technique in both Spark and Hive used to optimize the performance of the task. In bucketing buckets (clustering columns) determine data partitioning and prevent data shuffle. Based on the value of one or more bucketing columns, the data is allocated to a predefined number of buckets. Figure 1.1
What is a bucketing bucket?
In bucketing buckets ( clustering columns) determine data partitioning and prevent data shuffle. Based on the value of one or more bucketing columns, the data is allocated to a predefined number of buckets.
What is the difference between bucketing in Spark SQL?
Unlike bucketing in Apache Hive, Spark SQL creates the bucket files per the number of buckets and partitions. In other words, the number of bucketing files is the number of buckets multiplied by the number of task writers (one per partition). With bucketing, the Exchanges are no longer needed (as the tables are already pre-shuffled).
What is dataframewriter bucketby in spark?
DataFrameWriter.bucketBy and DataFrameWriter.sortBy simply set respective internal properties that eventually become a bucketing specification . Unlike bucketing in Apache Hive, Spark SQL creates the bucket files per the number of buckets and partitions.
What is difference between bucketing and partitioning in Spark?
At a high level, Hive Partition is a way to split the large table into smaller tables based on the values of a column(one partition for each distinct values) whereas Bucket is a technique to divide the data in a manageable form (you can specify how many buckets you want).
What is the use of bucketing?
Bucketing in hive is useful when dealing with large datasets that may need to be segregated into clusters for more efficient management and to be able to perform join queries with other large datasets. The primary use case is in joining two large datasets involving resource constraints like memory limits.
Can we do bucketing in Spark?
Bucketing in Spark is a way how to organize data in the storage system in a particular way so it can be leveraged in subsequent queries which can become more efficient. This efficiency improvement is specifically related to avoiding the shuffle in queries with joins and aggregations if the bucketing is designed well.
What is the difference between partitioning and bucketing?
Bucketing decomposes data into more manageable or equal parts. With partitioning, there is a possibility that you can create multiple small partitions based on column values. If you go for bucketing, you are restricting number of buckets to store the data. This number is defined during table creation scripts.
Is bucketing and clustering same?
“clustered by” clause is used to divide the table into buckets. Each bucket will be saved as a file under table directory. Bucketing can be done along with partitioning or without partitioning on Hive tables. Bucketed tables will create almost equally distributed data file parts.
How does bucketing improve performance?
Bucketing improves performance by shuffling and sorting data prior to downstream operations such as table joins. The tradeoff is the initial overhead due to shuffling and sorting, but for certain data transformations, this technique can improve performance by avoiding later shuffling and sorting.
What is meaning of bucketing?
What Is Bucketing? Bucketing is an unethical practice whereby a broker generates a profit by misleading their client about the execution of a particular trade. Specifically, it refers to a situation in which the broker confirms that a requested trade has taken place without actually executing that order.
What is full shuffle in Spark?
The Spark SQL shuffle is a mechanism for redistributing or re-partitioning data so that the data is grouped differently across partitions, based on your data size you may need to reduce or increase the number of partitions of RDD/DataFrame using spark.
What is cluster by in Spark?
Description. The CLUSTER BY clause is used to first repartition the data based on the input expressions and then sort the data within each partition. This is semantically equivalent to performing a DISTRIBUTE BY followed by a SORT BY.
What are three types of partitions?
Different Drive PartitionsPrimary Partition: Contains one file system and typically stores the boot files for the primary operating system. ... Extended Partition: A defined area where logical drives are stored. ... Logical Partition: Can be used to store data, but can't boot an operating system.
Can we use bucketing without partitioning?
Bucketing can also be done even without partitioning on Hive tables. Bucketed tables allow much more efficient sampling than the non-bucketed tables.
What are the two types of partitioning?
The two types of partitions are: Primary partition. Extended partition. A primary partition is a partition on which you can install an operating system.
What is a bucketing strategy?
The bucket strategy divides your savings into three buckets, which are each invested differently. Here's a look at the goal of each retirement bucket. The immediate bucket. The first bucket of cash and cash equivalents provides a chance to access funds when needed.
What is bucketing in SQL?
Bucketing is an optimization technique in Spark SQL that uses buckets and bucketing columns to determine data partitioning. When applied properly bucketing can lead to join optimizations by avoiding shuffles (aka exchanges) of tables participating in the join.
What is bucketing system?
The “Bucket System” is a way to do estimation of large numbers of items with a small to medium sized group of people, and to do it quickly. The Bucket System has the following qualities which make it particularly suitable for use in Agile environments: It's fast!
What is bucketing in Hadoop?
The bucketing in Hive is a data organizing technique. It is similar to partitioning in Hive with an added functionality that it divides large datasets into more manageable parts known as buckets. So, we can use bucketing in Hive when the implementation of partitioning becomes difficult.
What is bucketing?
Bucketing is a technique in both Spark and Hive used to optimize the performance of the task. In bucketing buckets ( clustering columns) determine data partitioning and prevent data shuffle. Based on the value of one or more bucketing columns, the data is allocated to a predefined number of buckets.
Bucketing has two key benefits
Improved query performance: At the time of joins, we can specify the number of buckets explicitly on the same bucketed columns. Since each bucket contains an equal size of data, map-side joins perform better than a non-bucketed table on a bucketed table.
Spark DAG stages analysis
Without Bucketing:- We will create two datasets without bucketing and perform join, groupBy, and distinct transformation.
Spark SQL bucketing limitations
The bucketing technique in Spark SQL is different from Hive which gives way to an expensive migration process from Hive to Spark SQL.
Conclusion
We should use bucketing when we have multi-joins and/or transformations that involve data shuffling and have the same column in joins and/or in transformation as we have in a bucket.
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Spark join without buckets
Let's first look into one example of INNER JOIN of two non-bucketing tables in Spark SQL. The following is code snippet:
Spark join with bucketed tables
Let's create a similar script using bucketBy API when saving into Hive tables.
Bucket pruning
At last, let's explore bucket pruning feature. Bucket pruning feature will select the required buckets if we add filters on bucket columns.
Summary
I hope you now have a good understanding of Spark bucketing and bucket pruning features. If you have any questions, feel free to post a comment.
What is bucketing in Spark?
Let’s start with this simple question. Bucketing in Spark is a way how to organize data in the storage system in a particular way so it can be leveraged in subsequent queries which can become more efficient. This efficiency improvement is specifically related to avoiding the shuffle in queries with joins and aggregations if the bucketing is designed well.
How does bucketing work in Spark?
Having a specific row, do we know in which bucket it will end up? Well, yes! Roughly speaking, Spark is using a hash function that is applied on the bucketing field and then computes this hash value modulo number of buckets that should be created (hash (x) mod n). This modulo operation ensures that no more than the specified number of buckets are created. For the sake of simplicity, let’s first assume that after applying the hash function we get these values: (1, 2, 3, 4, 5, 6 ) and we want to create 4 buckets, so we will compute modulo 4. Modulo function returns the remainder after integer division:
What is bucket pruning?
Bucket pruning (see Jira )— reduce I/O with a filter on the bucketed field.
What happens if only one table is bucketed and the other is not?
If the number of buckets is greater or equal to the number of shuffle partitions, Spark will shuffle only one side of the join — the table that was not bucketed. However, if the number of buckets is less than the number of shuffle partitions, Spark will shuffle both tables and will not leverage the fact that one of the tables is already well distributed. The default number of shuffle partitions is 200 and it can be controlled with this configuration setting:
Why is bucketing important?
The main goal of bucketing is to speed up queries and gain performance improvements. There are two main areas where bucketing can help, the first one is to avoid shuffle in queries with joins and aggregations, the second one is to reduce the I/O with a feature called bucket pruning.
How does Spark know if a table is bucketed?
If the table is not bucketed, Spark will have to scan the entire table to find this record and if the table is large, it can take many tasks that will have to be launched and executed. On the other hand, if the table is bucketed, Spark will know immediately to which bucket this row belongs (Spark computes the hash function with the modulo to see directly the bucket number) and will scan files only from the corresponding bucket. And how does Spark know which files belong to which bucket? Well, each file name has a specific structure and contains information not only about the bucket to which it belongs, but also which task produced the file as you can see from this picture:
What does "disable bucketing" mean in Jira?
Enable/disable bucketing by a rule (see Jira) — a rule that will turn off bucketing if it cannot be leveraged in the query.
What is bucketing in data?
Bucketing is an optimization technique that uses buckets (and bucketing columns) to determine data partitioning and avoid data shuffle.
What is bucket pruning in Spark?
As of Spark 2.4, Spark SQL supports bucket pruning to optimize filtering on bucketed column (by reducing the number of bucket files to scan).
How many partitions are there in a bucketed table?
The number of partitions of a bucketed table is exactly the number of buckets.
How many exchanges and sorts are there in SPARK 24025?
There are two exchanges and sorts which makes the above use case almost unusable. I filed an issue at SPARK-24025 Join of bucketed and non-bucketed tables can give two exchanges and sorts for non-bucketed side .
What is the use of SQLConf.bucketingEnabled?
Use SQLConf.bucketingEnabled to access the current value of spark.sql.sources.bucketing.enabled property.
Which execution planning strategy is responsible for selecting only LogicalRelations over HadoopFsRelation with the bucket?
FileSourceStrategy execution planning strategy is responsible for selecting only LogicalRelations over HadoopFsRelation with the bucketing specification with the following:
Does DataframeWriter support bucketing?
Bucketing is not supported for DataFrameWriter.save, DataFrameWriter.insertInto and DataFrameWriter.jdbc methods.
Apache Spark: Bucketing and Partitioning
Overview of partitioning and bucketing strategy to maximize the benefits while minimizing adverse effects. if you can reduce the overhead of shuffling, need for serialization, and network traffic, then why not. in the end Performance, better cluster utilization, and cost-efficiency beat it all.
Partitioning
in a distributed system, partitioning refers to dividing into parts (useful only when a dataset is reused multiple times).
Bucketing
If you have a use case to Join certain input / output regularly, then using bucketBy is a good approach. here we are forcing the data to be partitioned into the desired number of buckets.
List of Transformations by bucketing
bucketing can be useful when we need to perform multi-joins and/or transformations that involve data shuffling and have the same column in joins and/or in transformation as we have in a bucket. bucketing is not required if we don’t have the same column in joins/transformations.
What happens when Spark writes data to a bucketing table?
When Spark writes data to a bucketing table, it can generate tens of millions of small files which are not supported by HDFS;
Why do we use bucketing in SQL?
Bucketing is commonly used in Hive and Spark SQL to improve performance by eliminating Shuffle in Join or group-by-aggregate scenario. This is ideal for a variety of write-once and read-many datasets at Bytedance.
Why is exchange introduced in Spark?
In this example, exchange will be introduced because after Union the outputPartitioning and the outputOrdering will be set to unknown, and Spark SQL cannot know that the underlying tables are bucketed table, so the exchange will be introduced. Let me introduce how we optimize bucketing at ByteDance.
How many files are in a hive bucket?
Previously I introduced that the two difference is that the file number is different. For Hive there will be, it’s only one file for each bucket but for Spark there will be more than one files for each bucket. So we need to ensure that in Spark each bucket will consist of exactly one file. And another thing we need to know is that Spark use Murmur3Hash and Hive use HiveHash, so we changed Spark SQL
What are the limitations of Spark?
However, Spark SQL bucketing has various limitations: 1 The bucketing mechanism in Spark SQL is different from the one in Hive so that migration from Hive to Spark SQL is expensive; 2 Spark SQL bucketing requires sorting on read time which greatly degrades the performance; 3 When Spark writes data to a bucketing table, it can generate tens of millions of small files which are not supported by HDFS; 4 Bucket joins are triggered only when the two tables have the same number of bucket; 5 It requires the bucket key set to be identical with the join key set or grouping key set. Over the last year, we have added a series of optimizations in Apache Spark to eliminate the above limitations so that the new bucketing mechanism can cover more scenarios. And the new bucketing make Hive to Spark SQL migration more smooth.
Does Spark write to bucketed table?
Spark will write the data to bucketed table in the same way as Hive. Firstly, we change the required distribution of InsertIntoHiveTable plan and we set, and we set the,
Does Spark require a bucket?
Another limitation is that Spark SQL requires most of the table are bucketed on the same key set as the join key set. For example, if both table are bucketed on a user_id but we want to join them on user_id and location_id, then exchange will introduce for both of them.
What is bucketing in SQL?
Bucketing is an optimization technique in Spark SQL that uses buckets and bucketing columns to determine data partitioning. The bucketing concept is one of the optimization technique that use bucketing to optimize joins by avoiding shuffles of the tables participating in the join.
Can you create buckets in a dataframe?
You can create bucketing on the DataFrame. This is one of the options that you can use when you are working with DataFrames instead of tables.
Does Spark support clustering?
Spark SQL supports clustering column values using bucketing concept. Bucketing and partition is similar to that of Hive concept, but with syntax change. In this article, we will check Apache Spark SQL Bucketing support in different versions of Spark.
Which is better: bucketing or non-bucket?
On a larger table, creating a bucketing gives you 2-3x better query performance than a non-bucket table.
How to Decide the Number of Buckets?
When you decide to create bucket on Hive table, million dollar question you should ask to yourself is how many buckets to create? so let’s see how to decide the number of buckets to create.
Why create a bucket on top of partitioned table?
Create a bucket on top of the Partitioned table to further divide the table for better query performance.
What is a hive bucket?
Hive Bucketing is a way to split the table into a managed number of clusters with or without partitions. With partitions, Hive divides (creates a directory) the table into smaller parts for every distinct value of a column whereas with bucketing you can specify the number of buckets to create at the time of creating a Hive table.
Where is each bucket stored?
Each bucket is stored as a file within the table’s directory or the partitions directories on HDFS.
Can you control the number of partitions in a hive bucket?
Before jumping into the Advantages of Hive bucketing, first let’s see the limitation of Partition, with the partition you cannot control the number of partitions as it creates a partition for every distinct value of the partitioned column; which ideally creates a subdirectory for each partition inside the table directory on HDFS.
Is loading data into a bucket table the same as inserting data into a table?
Loading/inserting data into the Bucketing table would be the same as inserting data into the table.
What does the number of buckets do in Spark?from legendu.net
The number of buckets helps guide Spark engine on parallel execution level.
Where is a bucket stored?from sparkbyexamples.com
Each bucket is stored as a file within the table’s directory or the partitions directories. Note that partition creates a directory and you can have a partition on one or more columns; these are some of the differences between Hive partition and bucket.
Why do we use partitioning and bucketing in hive?from sparkbyexamples.com
Both Partitioning and Bucketing in Hive are used to improve performance by eliminating table scans when dealing with a large set of data on a Hadoop file system (HDFS). The major difference between Partitioning vs Bucketing lives in the way how they split the data.
What is a hive bucket?from sparkbyexamples.com
Hive Bucketing a.k.a (Clustering) is a technique to split the data into more manageable files, (By specifying the number of buckets to create). The value of the bucketing column will be hashed by a user-defined number into buckets.
What is a hive partition?from sparkbyexamples.com
Hive Partition is a way to organize large tables into smaller logical tables based on values of columns; one logical table (partition) for each distinct value. In Hive, tables are created as a directory on HDFS. A table can have one or more partitions that correspond to a sub-directory for each partition inside a table directory.
How does Dataframe.write.bucketBy work?from legendu.net
The above issue is not present when you DataFrame.write.bucketBy as DataFrame.write.bucketBy works by calculating hash code. There will always be the exact number of buckets/partitions on the disk as you specifed when you call the function DataFrame.write.bucketBy.
How does partition affect Spark?from legendu.net
When reading a table to Spark, the number of partitions in memory equals to the number of files on disk if each file is smaller than the block size, otherwise, there will be more partitions in memory than the number of files on disk.
