How does data distribution happens in bucketing in HIVE? - hadoop

Question

I have created a table as below with 3 buckets, and loaded some data into it.
create table testBucket (id int,name String)
partitioned by (region String)
clustered by (id) into 3 buckets;
I have set bucketing property as well. $set hive.enforce.bucketing=true;
But when I listed the table files in HDFS I could see that that 3 files are creates as I have mentioned 3 buckets.
But data got loaded in only one file and rest 2 files are just empty. So I am confused why my data got loaded into only file?
So could someone please explain me how data distribution happens in bucketing?
[test#localhost user]$ hadoop fs -ls /user/hive/warehouse/database2.db/buckettab/region=USA
Found 3 items
-rw-r--r-- 1 user supergroup 38 2016-06-27 08:34 /user/hive/warehouse/database2.db/buckettab/region=USA/000000_0
-rw-r--r-- 1 user supergroup 0 2016-06-27 08:34 /user/hive/warehouse/database2.db/buckettab/region=USA/000001_0
-rw-r--r-- 1 user supergroup 0 2016-06-27 08:34 /user/hive/warehouse/database2.db/buckettab/region=USA/000002_0

Answer 1

Bucketing is a method to evenly distributed the data across many files. Create multiple buckets and then place each record into one of the buckets based on some logic mostly some hashing algorithm.
Bucketing feature of Hive can be used to distribute/organize the table/partition data into multiple files such that similar records are present in the same file. While creating a Hive table, a user needs to give the columns to be used for bucketing and the number of buckets to store the data into. Which records go to which bucket are decided by the Hash value of columns used for bucketing.
[Hash(column(s))] MOD [Number of buckets]
Hash value for different columns types is calculated differently. For int columns, the hash value is equal to the value of int. For String columns, the hash value is calculated using some computation on each character present in the String.
Data for each bucket is stored in a separate HDFS file under the table directory on HDFS. Inside each bucket, we can define the arrangement of data by providing the SORT BY column while creating the table.
Lets See an Example
Creating a Hive table using bucketing
For creating a bucketed table, we need to use CLUSTERED BY clause to define the columns for bucketing and provide the number of buckets. Following query creates a table Employee bucketed using the ID column into 5 buckets.
CREATE TABLE Employee(
ID BIGINT,
NAME STRING,
AGE INT,
SALARY BIGINT,
DEPARTMENT STRING
)
COMMENT 'This is Employee table stored as textfile clustered by id into 5 buckets'
CLUSTERED BY(ID) INTO 5 BUCKETS
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE;
Inserting data into a bucketed table
We have following data in Employee_old table.
0: jdbc:hive2://localhost:10000> select * from employee_old;
+------------------+--------------------+-------------------+----------------------+--------------------------+--+
| employee_old.id | employee_old.name | employee_old.age | employee_old.salary | employee_old.department |
+------------------+--------------------+-------------------+----------------------+--------------------------+--+
| 1 | Sudip | 34 | 62000 | HR |
| 2 | Suresh | 45 | 76000 | FINANCE |
| 3 | Aarti | 25 | 37000 | BIGDATA |
| 4 | Neha | 27 | 39000 | FINANCE |
| 5 | Rajesh | 29 | 59000 | BIGDATA |
| 6 | Suman | 37 | 63000 | HR |
| 7 | Paresh | 42 | 71000 | BIGDATA |
| 8 | Rami | 33 | 56000 | HR |
| 9 | Arpit | 41 | 46000 | HR |
| 10 | Sanjeev | 51 | 99000 | FINANCE |
| 11 | Sanjay | 32 | 67000 | FINANCE |
+------------------+--------------------+-------------------+----------------------+--------------------------+--+
We will select data from the table Employee_old and insert it into our bucketed table Employee.
We need to set the property ‘hive.enforce.bucketing‘ to true while inserting data into a bucketed table. This will enforce bucketing, while inserting data into the table.
Set the property
0: jdbc:hive2://localhost:10000> set hive.enforce.bucketing=true;
Insert data into Bucketed table employee
0: jdbc:hive2://localhost:10000> INSERT OVERWRITE TABLE Employee SELECT * from Employee_old;
Verify the Data in Buckets
Once we execute the INSERT query, we can verify that 5 files are created Under the Employee table directory on HDFS.
Name Type
000000_0 file
000001_0 file
000002_0 file
000003_0 file
000004_0 file
Each file represents a bucket. Let us see the contents of these files.
Content of 000000_0
All records with Hash(ID) mod 5 == 0 goes into this file.
5,Rajesh,29,59000,BIGDATA
10,Sanjeev,51,99000,FINANCE
Content of 000001_0
All records with Hash(ID) mod 5 == 1 goes into this file.
1,Sudip,34,62000,HR
6,Suman,37,63000,HR
11,Sanjay,32,67000,FINANCE
Content of 000002_0
All records with Hash(ID) mod 5 == 2 goes into this file.
2,Suresh,45,76000,FINANCE
7,Paresh,42,71000,BIGDATA
Content of 000003_0
All records with Hash(ID) mod 5 == 3 goes into this file.
3,Aarti,25,37000,BIGDATA
8,Rami,33,56000,HR
Content of 000004_0
All records with Hash(ID) mod 5 == 4 goes into this file.
4,Neha,27,39000,FINANCE
9,Arpit,41,46000,HR

Answer 2

I feel all ID MOD 3 will be same for USA partition (region=USA) in sample data.
Bucket number is determined by the expression hash_function(bucketing_column) mod num_buckets. (There's a '0x7FFFFFFF in there too, but that's not that important). The hash_function depends on the type of the bucketing column. For an int, it's easy, hash_int(i) == i. For example, if user_id were an int, and there were 10 buckets, we would expect all user_id's that end in 0 to be in bucket 1, all user_id's that end in a 1 to be in bucket 2, etc. For other datatypes, it's a little tricky. In particular, the hash of a BIGINT is not the same as the BIGINT. And the hash of a string or a complex datatype will be some number that's derived from the value, but not anything humanly-recognizable. For example, if user_id were a STRING, then the user_id's in bucket 1 would probably not end in 0. In general, distributing rows based on the hash will give you a even distribution in the buckets.

Answer 3

Take a look at the language Manual here
It states:
How does Hive distribute the rows across the buckets? In general, the bucket number is determined by the expression hash_function(bucketing_column) mod num_buckets. (There's a '0x7FFFFFFF in there too, but that's not that important). The hash_function depends on the type of the bucketing column. For an int, it's easy, hash_int(i) == i. For example, if user_id were an int, and there were 10 buckets, we would expect all user_id's that end in 0 to be in bucket 1, all user_id's that end in a 1 to be in bucket 2, etc. For other datatypes, it's a little tricky. In particular, the hash of a BIGINT is not the same as the BIGINT. And the hash of a string or a complex datatype will be some number that's derived from the value, but not anything humanly-recognizable. For example, if user_id were a STRING, then the user_id's in bucket 1 would probably not end in 0. In general, distributing rows based on the hash will give you a even distribution in the buckets.
In your case because you are clustering by Id which is an Int and then you're bucketing it into 3 buckets only it looks like all values are being hashed into one of these buckets. To ensure this is working, add some rows that have different ids from the ones you have in the file and increase the number of buckets and see if they get hashed into separate files this time around.