This is the interview question I faced, if we have 1 TB data in HDFS. Which type of method in hive gives us faster performance i.e partitioning or bucketing ?
I told them depending upon data we choose either partitioning or bucketing .But the interviewer didn't satisfied with my answer.
What should be proper answer (along with example) for it?
Your answer is correct that - It really depends on the data and what exactly you want to do with the data.
Partitioning is used for distributing load horizontally in a logical fashion. It optimizes the performance, but sometime it could lead to partition having very less amount of the within them. This results into bad performance, as the mapreduce works on bigger files than many small files.
Here, bucketing can help, because bucketing guarantee that all the data for the bucketing column remains together. E.g. if we bucket the employee table and use emp_id as the bucketing column, the value of this column will be hashed by a user-defined number of buckets (which must be optimized considering number of records). Records with the same emp_id will always be stored in the bucket. At the same time, one bucket may have many emp_id together having a more optimized chunk of data for mapreduce processing. bucketing is specially helpful, if you want to perform map-side join.
Your answer is correct--
Hive partitioning is an effective method to improve the query performance on larger tables . Partitioning allows you to store data in separate sub-directories under table location. It greatly helps the queries which are queried upon the partition key(s).
Bucketing improves the join performance if the bucket key and join keys are common. Bucketing in Hive distributes the data in different buckets based on the hash results on the bucket key. It also reduces the I/O scans during the join process if the process is happening on the same keys (columns).
I understand the concepts of partitioning and bucketing in Hive tables. But what I'd like to know is "when do we for partition and when do we go for bucketing ?"
What are ideal scenarios that can be said as suitable for partitioning and bucketing ?
The main reasons in which one uses partition and bucketing.
Partition:
Partitioning of table data is done for distributing load horizontally .
Example: If we have a very large table names as "Parts" and often we run "where" queries that restricts the results to a particular Part Type.
For a faster query response the table can be partitioned by (PART_TYPE STRING).Once you partition the table it changes the way Hive structures the data storage and Hive now will create sub-directories which will reflect the structure of the partition like:
.../Parts/PART_TYPE = Engine-Part
.../Parts/Part_Type = Brakes
So,now if you run a query on table "Parts" with WHERE PART_TYPE = 'Engine-Part'
, it will only scan the contents of one directory PART_TYPE = 'Engine-Part'
Partitioning feature is useful in Hive. but at the same time it may take long time to execute other queries.
Another drawback is if we create too many partitions which in turn creates large number of Hadoop files and directories that got created unnecessarily and it becomes an overhead to NameNode since NameNode must keep all metdatafiles for the file system in memory.
Bucketing:
Bucketing is another technique which can be used to further divide the data into more manageable form.
Example: Suppose the table "part_sale" has a top level partition of "sale_date" and it is further partitioned into "part_type" as second level partition.
This will lead to too many small partitions .
.../part_sale/sale-date = 2017-04-18/part_type = engine_part1
.../part_sale/sale-date = 2017-04-18/part_type = engine_part2
.../part_sale/sale-date = 2017-04-18/part_type = engine_part3
.../part_sale/sale-date = 2017-04-18/part_type = engine_part4
If we bucket the "part_sale" table ,and use "part_type" as our bucketing column of the table.The value of this column will be hashed by a user-defined number into buckets.Records with the same "part_type" will always be stored in same bucket.You can specify the number of buckets at the time of table creation so that number of buckets are fixed and there is no fluctuation with data.
Partitioning in Hive :-
If we are dealing with a large table and often run queries with WHERE clauses that restrict the results to a particular partitioned column/columns, then we should leverage the partition concept of hive . For a faster query response Hive table can be PARTITIONED BY (partition_cols_name).Its helps to organize the data in logical fashion and when we query the partitioned table using partition column, it allows hive to skip all but relevant sub-directories and files, so scan becomes easy if partition is done properly. Should be done when the cardinality (number of possible values a field can have ) is not high. Else if there are too many partitions, then it is an overhead on the namenode.
Bucketing in Hive :-
If you want to segregate the data on a field which has high cardinality (number of possible values a field can have ), then we should use bucketing. If we want only a sample of data according to some specific fields and not the entire data , bucketing can be a good option. If some map-side joins are involved, then bucketed tables are a good option.
Partitioning helps in elimination of data, if used in WHERE clause, where as bucketing helps in organizing data in each partition into multiple files, so as same set of data is always written in same bucket. Helps a lot in joining of columns.
Hive Buckets is nothing but another technique of decomposing data or decreasing the data into more manageable parts or equal parts.For example we have table with columns like date,employee_name,employee_id,salary,leaves etc . In this table just use date column as the top-level partition and the employee_id as the second-level partition leads to too many small partitions. We can use HASH value for bucketing or a range to bucket the data.
Hive partitioning and Bucketing is ,when we do partitioning, we create a partition for each unique value of the column. But there may be situation where we need to create lot of tiny partitions. But if you use bucketing, you can limit it to a number which you choose and decompose your data into those buckets. In hive a partition is a directory but a bucket is a file.
In hive, bucketing does not work by default. You will have to set following variable to enable bucketing. set hive.enforce.bucketing=true;
PARTITIONING will be used when there are few unique values in the Column - which you want to load with your required WHERE clause
BUCKETING will be used if there are multiple unique values in your Where clause
I know that the number of map tasks is the same as the number of input splits given by the input format. When performing an operation on a partitioned or bucketed hive table how does the InputFormat class calculate input Splits as the data is in the form of files in a directory for partitioned or bucketed data? Is there any relation between the input splits(number of map tasks) and the number of partitions or buckets?
The short answer is 'sortof'.. hive delegates the splits to hadoop, and hadoop only cares about how much data is in that partitions, not really about partitions and buckets. The amount of data depends indirectly on the number of partitions, so to answer your question more correctly, it does not directly depend on the number of partitions.
When executing a query, to make the splits hive uses by default CombineHiveInputFormat
which is actually only a wrapper around Hadoop's CombineFileInputFormat.
So actually hive will delegate to hadoop how to make the splits.
Hadoop's CombineFileInputFormat will group smaller files together so the splits will be according to what's configured as minimum split size.
Note that it belongs to hadoop, not hive, therefore has no knowledge of buckets, but it will just group files based on their size and locality (rack, etc), since it's better of a split is actually all on the same node or at least on the same rack.
You can have a look at how the splits are created in the function getSplits here.
As far as i have got to understand from the hadoop tuitorial, it takes the overall size of the input files and then divides them into the blocks/chunks then these block are replicated on different nodes.However i want to realize data distribution model according to the below given requirement -
(a) Case one : Each file is partitioned into the nodes in the cluster equally
-- so that each map gets this partition of table to be accessed. is it possible ?
(b) Case two : Each file is fully replicated in two or more nodes but not all nodes.
so that each map access some part of table on each node. is it possible ?
HDFS does not store tables, it stores files. Higher level projects offer 'relational tables', like Hive. Hive does allow you to partition a table stored on HDFS, see Hive Tutorial.
That being said, you should not tie partitioning to number of nodes in the cluster. Nodes come and go, clusters grow and shrink. Partitioned relational tables partition/bucket by natural boundaries w/o relation to cluster size. Import, export, daily operations all play a role in partitioning (and usually a much bigger role then cluster size). Even a single table (file) can well spread on each node of the cluster.
If you want to tune a MR job for optimal split size/location, there are plenty of ways to do that. You still have a lot to read, you are optimizing too early.
I know both is performed on a column in the table but how is each operation different.
Partitioning data is often used for distributing load horizontally, this has performance benefit, and helps in organizing data in a logical fashion. Example: if we are dealing with a large employee table and often run queries with WHERE clauses that restrict the results to a particular country or department . For a faster query response Hive table can be PARTITIONED BY (country STRING, DEPT STRING). Partitioning tables changes how Hive structures the data storage and Hive will now create subdirectories reflecting the partitioning structure like
.../employees/country=ABC/DEPT=XYZ.
If query limits for employee from country=ABC, it will only scan the contents of one directory country=ABC. This can dramatically improve query performance, but only if the partitioning scheme reflects common filtering. Partitioning feature is very useful in Hive, however, a design that creates too many partitions may optimize some queries, but be detrimental for other important queries. Other drawback is having too many partitions is the large number of Hadoop files and directories that are created unnecessarily and overhead to NameNode since it must keep all metadata for the file system in memory.
Bucketing is another technique for decomposing data sets into more manageable parts. For example, suppose a table using date as the top-level partition and employee_id as the second-level partition leads to too many small partitions. Instead, if we bucket the employee table and use employee_id as the bucketing column, the value of this column will be hashed by a user-defined number into buckets. Records with the same employee_id will always be stored in the same bucket. Assuming the number of employee_id is much greater than the number of buckets, each bucket will have many employee_id. While creating table you can specify like CLUSTERED BY (employee_id) INTO XX BUCKETS; where XX is the number of buckets . Bucketing has several advantages. The number of buckets is fixed so it does not fluctuate with data. If two tables are bucketed by employee_id, Hive can create a logically correct sampling. Bucketing also aids in doing efficient map-side joins etc.
There are a few details missing from the previous explanations.
To better understand how partitioning and bucketing works, you should look at how data is stored in hive.
Let's say you have a table
CREATE TABLE mytable (
name string,
city string,
employee_id int )
PARTITIONED BY (year STRING, month STRING, day STRING)
CLUSTERED BY (employee_id) INTO 256 BUCKETS
then hive will store data in a directory hierarchy like
/user/hive/warehouse/mytable/y=2015/m=12/d=02
So, you have to be careful when partitioning, because if you for instance partition by employee_id and you have millions of employees, you'll end up having millions of directories in your file system.
The term 'cardinality' refers to the number of possible value a field can have. For instance, if you have a 'country' field, the countries in the world are about 300, so cardinality would be ~300. For a field like 'timestamp_ms', which changes every millisecond, cardinality can be billions. In general, when choosing a field for partitioning, it should not have a high cardinality, because you'll end up with way too many directories in your file system.
Clustering aka bucketing on the other hand, will result with a fixed number of files, since you do specify the number of buckets. What hive will do is to take the field, calculate a hash and assign a record to that bucket.
But what happens if you use let's say 256 buckets and the field you're bucketing on has a low cardinality (for instance, it's a US state, so can be only 50 different values) ? You'll have 50 buckets with data, and 206 buckets with no data.
Someone already mentioned how partitions can dramatically cut the amount of data you're querying. So in my example table, if you want to query only from a certain date forward, the partitioning by year/month/day is going to dramatically cut the amount of IO.
I think that somebody also mentioned how bucketing can speed up joins with other tables that have exactly the same bucketing, so in my example, if you're joining two tables on the same employee_id, hive can do the join bucket by bucket (even better if they're already sorted by employee_id since it's going to mergesort parts that are already sorted, which works in linear time aka O(n) ).
So, bucketing works well when the field has high cardinality and data is evenly distributed among buckets. Partitioning works best when the cardinality of the partitioning field is not too high.
Also, you can partition on multiple fields, with an order (year/month/day is a good example), while you can bucket on only one field.
Before going into Bucketing, we need to understand what Partitioning is. Let us take the below table as an example. Note that I have given only 12 records in the below example for beginner level understanding. In real-time scenarios you might have millions of records.
PARTITIONING
---------------------
Partitioning is used to obtain performance while querying the data. For example, in the above table, if we write the below sql, it need to scan all the records in the table which reduces the performance and increases the overhead.
select * from sales_table where product_id='P1'
To avoid full table scan and to read only the records related to product_id='P1' we can partition (split hive table's files) into multiple files based on the product_id column. By this the hive table's file will be split into two files one with product_id='P1' and other with product_id='P2'. Now when we execute the above query, it will scan only the product_id='P1' file.
../hive/warehouse/sales_table/product_id=P1
../hive/warehouse/sales_table/product_id=P2
The syntax for creating the partition is given below. Note that we should not use the product_id column definition along with the non-partitioned columns in the below syntax. This should be only in the partitioned by clause.
create table sales_table(sales_id int,trans_date date, amount int)
partitioned by (product_id varchar(10))
Cons : We should be very careful while partitioning. That is, it should not be used for the columns where number of repeating values are very less (especially primary key columns) as it increases the number of partitioned files and increases the overhead for the Name node.
BUCKETING
------------------
Bucketing is used to overcome the cons that I mentioned in the partitioning section. This should be used when there are very few repeating values in a column (example - primary key column). This is similar to the concept of index on primary key column in the RDBMS. In our table, we can take Sales_Id column for bucketing. It will be useful when we need to query the sales_id column.
Below is the syntax for bucketing.
create table sales_table(sales_id int,trans_date date, amount int)
partitioned by (product_id varchar(10)) Clustered by(Sales_Id) into 3 buckets
Here we will further split the data into few more files on top of partitions.
Since we have specified 3 buckets, it is split into 3 files each for each product_id. It internally uses modulo operator to determine in which bucket each sales_id should be stored. For example, for the product_id='P1', the sales_id=1 will be stored in 000001_0 file (ie, 1%3=1), sales_id=2 will be stored in 000002_0 file (ie, 2%3=2),sales_id=3 will be stored in 000000_0 file (ie, 3%3=0) etc.
I think I am late in answering this question, but it keep coming up in my feed.
Navneet has provided excellent answer. Adding to it visually.
Partitioning helps in elimination of data, if used in WHERE clause, where as bucketing helps in organizing data in each partition into multiple files, so as same set of data is always written in same bucket. Helps a lot in joining of columns.
Suppose, you have a table with five columns, name, server_date, some_col3, some_col4 and some_col5. Suppose, you have partitioned the table on server_date and bucketed on name column in 10 buckets, your file structure will look something like below.
server_date=xyz
00000_0
00001_0
00002_0
........
00010_0
Here server_date=xyz is the partition and 000 files are the buckets in each partition. Buckets are calculated based on some hash functions, so rows with name=Sandy will always go in same bucket.
Hive Partitioning:
Partition divides large amount of data into multiple slices based on value of a table column(s).
Assume that you are storing information of people in entire world spread across 196+ countries spanning around 500 crores of entries. If you want to query people from a particular country (Vatican city), in absence of partitioning, you have to scan all 500 crores of entries even to fetch thousand entries of a country. If you partition the table based on country, you can fine tune querying process by just checking the data for only one country partition. Hive partition creates a separate directory for a column(s) value.
Pros:
Distribute execution load horizontally
Faster execution of queries in case of partition with low volume of data. e.g. Get the population from "Vatican city" returns very fast instead of searching entire population of world.
Cons:
Possibility of too many small partition creations - too many directories.
Effective for low volume data for a given partition. But some queries like group by on high volume of data still take long time to execute. e.g. Grouping of population of China will take long time compared to grouping of population in Vatican city. Partition is not solving responsiveness problem in case of data skewing towards a particular partition value.
Hive 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.
Pros
Due to equal volumes of data in each partition, joins at Map side will be quicker.
Faster query response like partitioning
Cons
You can define number of buckets during table creation but loading of equal volume of data has to be done manually by programmers.
The difference is bucketing divides the files by Column Name, and partitioning divides the files under By a particular value inside table
Hopefully I defined it correctly
There are great responses here. I would like to keep it short to memorize the difference between partition & buckets.
You generally partition on a less unique column. And bucketing on most unique column.
Example if you consider World population with country, person name and their bio-metric id as an example. As you can guess, country field would be the less unique column and bio-metric id would be the most unique column. So ideally you would need to partition the table by country and bucket it by bio-metric id.
Using Partitions in Hive table is highly recommended for below reason -
Insert into Hive table should be faster ( as it uses multiple threads
to write data to partitions )
Query from Hive table should be efficient with low latency.
Example :-
Assume that Input File (100 GB) is loaded into temp-hive-table and it contains bank data from across different geographies.
Hive table without Partition
Insert into Hive table Select * from temp-hive-table
/hive-table-path/part-00000-1 (part size ~ hdfs block size)
/hive-table-path/part-00000-2
....
/hive-table-path/part-00000-n
Problem with this approach is - It will scan whole data for any query you run on this table. Response time will be high compare to other approaches where partitioning and Bucketing are used.
Hive table with Partition
Insert into Hive table partition(country) Select * from temp-hive-table
/hive-table-path/country=US/part-00000-1 (file size ~ 10 GB)
/hive-table-path/country=Canada/part-00000-2 (file size ~ 20 GB)
....
/hive-table-path/country=UK/part-00000-n (file size ~ 5 GB)
Pros - Here one can access data faster when it comes to querying data for specific geography transactions.
Cons - Inserting/querying data can further be improved by splitting data within each partition. See Bucketing option below.
Hive table with Partition and Bucketing
Note: Create hive table ..... with "CLUSTERED BY(Partiton_Column) into 5 buckets
Insert into Hive table partition(country) Select * from temp-hive-table
/hive-table-path/country=US/part-00000-1 (file size ~ 2 GB)
/hive-table-path/country=US/part-00000-2 (file size ~ 2 GB)
/hive-table-path/country=US/part-00000-3 (file size ~ 2 GB)
/hive-table-path/country=US/part-00000-4 (file size ~ 2 GB)
/hive-table-path/country=US/part-00000-5 (file size ~ 2 GB)
/hive-table-path/country=Canada/part-00000-1 (file size ~ 4 GB)
/hive-table-path/country=Canada/part-00000-2 (file size ~ 4 GB)
/hive-table-path/country=Canada/part-00000-3 (file size ~ 4 GB)
/hive-table-path/country=Canada/part-00000-4 (file size ~ 4 GB)
/hive-table-path/country=Canada/part-00000-5 (file size ~ 4 GB)
....
/hive-table-path/country=UK/part-00000-1 (file size ~ 1 GB)
/hive-table-path/country=UK/part-00000-2 (file size ~ 1 GB)
/hive-table-path/country=UK/part-00000-3 (file size ~ 1 GB)
/hive-table-path/country=UK/part-00000-4 (file size ~ 1 GB)
/hive-table-path/country=UK/part-00000-5 (file size ~ 1 GB)
Pros - Faster Insert. Faster Query.
Cons - Bucketing will creating more files. There could be issue with many small files in some specific cases
Hope this will help !!