bucketing in hive example

(When using both partitioning and bucketing, each partition will be split into an equal number of buckets.) A table is bucketed on one or more columns with a fixed number of hash buckets. Below is a little advanced example of bucketing in Hive. What is the difference between partitioning and bucketing ... For bucket optimization to kick in when joining them: - The 2 tables must be bucketed on the same keys/columns. LanguageManual DDL BucketedTables - Apache Hive - Apache ... Partitioning vs bucketing.txt - Partitioning data is often ... Bucketing divides the whole data into specified number of small blocks. For example, bucketing by patient ID means we can quickly evaluate a user-based query by running it on a randomized sample of the total set of users. Buckets are basically folders containing files. It also reduces the I/O scans during the join process if the process is happening on the same keys (columns). All the same salary records will be stored in a similar bucket. As instructed by the ORDER BY clause, it goes through the Hive tables' columns to find and filter specific column values. Hive partitioning ensures you have data segregation, which can fasten the data analysis process. CREATE TABLE zipcodes ( RecordNumber int, Country string, City string, Zipcode int) PARTITIONED BY ( state string) CLUSTERED BY Zipcode INTO 10 BUCKETS ROW FORMAT DELIMITED FIELDS TERMINATED BY ','; We will see it in action. Bucketing in Hive - Study With Swati For a faster query response, the table can be partitioned by (ITEM_TYPE STRING). We can use TABLESAMPLE clause to bucket the table on the given column and get data from only some of the buckets. This is among the biggest advantages of bucketing. Example . -> All the same values of a bucketed column will go into same bucket. enforce. This approach does not scale in the following scenarios: The number of skewed keys is very large. SET hive.enforce.bucketing = true; or Set mapred.reduce.tasks = <<number of buckets>> And enable the bucketing using command Bucketing in Hive - BIG DATA PROGRAMMERS On above image, each file is a bucket which contains records for that specific bucket. Bucketing in Hive - What is Bucketing in Hive? [Example ... Data is divided into buckets based on a specified column in a table. Maddy's Experiences: Hive Bucketing with Example To accurately set the number of reducers while bucketing and land the data appropriately, we use "hive.enforce.bucketing = true". Whenever you write to a bucketed table, you need to make sure that you either set hive.enforce.bucketing to true, or set mapred.reduce.tasks to the number of buckets.//) Have one directory per skewed key, and the remaining keys go into a separate directory. In the above 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 do a mergesort which works in linear time). Example of Bucketing in Hive Taking an example, let us create a partitioned and a bucketed table named "student", CREATE TABLE student ( Student name, Roll_number int, Class int ) PARTITIONED BY (class int) CLUSTERED BY (roll_number) into 15 buckets row format delimited fields terminated by ',' stored as TEXTFILE This may burst into a situation where you might need to create thousands of tiny partitions. If you need a Hive query example, we've gathered five: ORDER BY: This syntax in HiveQL uses the SELECT statement to sort data. HIVE Bucketing also provides efficient sampling in Bucketing table than the non-bucketed tables. Bucketing is a technique in both Spark and Hive used to optimize the performance of the task. Each bucket is stored as a file in the partition directory. For example, if your HDFS block size is 256MB, even if n% of input size is only 100MB, you get 256MB of data. In the below sample code , a hash function will be done on the 'emplid' and similar ids will be placed in the same bucket. Trino | Improved Hive Bucketing Tip 4: Block Sampling Similarly, to the previous tip, we often want to sample data from only one table to explore queries and data. • Bucketing is best suited for sampling • Map-side joins can be done well with bucketing. Bucketing CTAS query results works well when you bucket data by the column that has high cardinality and evenly distributed values. In bucketing buckets ( clustering columns) determine data partitioning and prevent data shuffle. This video is all about "hive partition and bucketing example" topic information but we also try to cover the subjects:-when to use partition and bucketing i. Bucketing in Hive: Example #3. The number of buckets is fixed so it does not fluctuate with data. Hive Bucketing Explained with Examples — SparkByExamples HIVE Bucketing. simulink model of wind energy system with three-phase load / australia vs south africa rugby radio commentary . In other words, the number of bucketing files is the number of buckets multiplied by the number of task writers (one per partition). Launching Visual Studio Code. Now, based on the resulted value, the data is stored into the corresponding bucket. The range for a bucket is determined by the hash value of one or more columns in the dataset. Hive Query Example. Hive will guarantee that all rows which have the same hash will end up in the same . Spark SQL Bucketing on DataFrame. HIVE Bucketing improves the join performance if the bucket key and join keys are common. If the above condition is satisfied, then the joining operation of the tables can be performed at the mapper side only, otherwise, an inner join is performed. Bucketing in hive. Using Bucketing, Hive provides another technique to organize tables' data in more manageable way. 2.) We need to provide the required sample size in the queries. Now, if we want to perform partitioning on the basis of department column. Data is allocated among a specified number of buckets, according to values derived from one or more bucketing columns. Apache Hive, Apache Mesos, Akka Actors/Stream/HTTP, and Docker). When applied properly bucketing can lead to join optimizations by avoiding shuffles (aka exchanges) of tables participating in the join. Bucketing also aids in doing efficient map-side joins etc.-----Eample of PARTITONING AND BUCKETING: 95 down vote There are a few details missing from the previous explanations. In general,. Recipe Objective. Hive uses some hashing algorithm to generate a number in range of 1 to N buckets. We will use Pyspark to demonstrate the bucketing examples. Hive uses some hashing algorithm to generate a number in range of 1 to N buckets and based on the result of hashing, data is placed in a particular buckets as a file. Bucketing improves performance by shuffling and sorting data prior to downstream operations such as table joins. In Hive, we have to enable buckets by using the set.hive.enforce.bucketing=true; Step 1) Creating Bucket as shown below. The hash function output depends on the type of the column choosen. To make sure that bucketing of tableA is leveraged, we have two options, either we set the number of shuffle partitions to the number of buckets (or smaller), in our example 50, # if tableA is bucketed into 50 buckets and tableB is not bucketed spark.conf.set("spark.sql.shuffle.partitions", 50) tableA.join(tableB, joining_key) Bucketing is a concept of breaking data down into ranges which is called buckets. Create multiple buckets and then place each record into one of the buckets based on some logic mostly some hashing algorithm. c)create bucketed table . For example, for our orders table, we have specified to keep data in 4 buckets and this data should be grouped on basis of order it then hive will create 4 files and use Hash Algorithm to separate orders in 4 groups and write them into 4 files. e886b14 on Sep 28, 2017. Bucketing has several advantages. Partitioning data is often used for distributing load horizontally, this has performance benefit, and helps in organizing data in a logical fashion. A bucketed table can be created as in the below example: CREATE TABLE IF NOT EXISTS buckets_test.nytaxi_sample_bucketed ( trip_id INT, vendor_id STRING, pickup_datetime TIMESTAMP ) CLUSTERED BY (trip_id) INTO 20 BUCKETS STORED AS PARQUET And enable the bucketing using command. For example, a table definition in Presto syntax looks like this: CREATE TABLE page_views (user_id bigint, page_url varchar, dt date) . This is ideal for a variety of write-once and read-many datasets at Bytedance. CREATE TABLE bucketed_user ( Unlike bucketing in Apache Hive, Spark SQL creates the bucket files per the number of buckets and partitions. Bucketing is an optimization technique in both Spark and Hive that uses buckets (clustering columns) to determine data partitioning and avoid data shuffle.. For example, columns storing timestamp data could potentially have a very large number of distinct values, and their data is evenly distributed across the data set. Partitioning. gauravsinghaec Adding scripts and data-set for Hive Partitioning and Bucketing. You can nest buckets and see them as sub folders. You can use the buckets in sampling Hive table. hive with clause create view. Hive will calculate a hash for it and assign a record to that bucket. Yes, granularity of block sampling is at block level. As long as you use the syntax above and set hive.enforce.bucketing = true (for Hive 0.x and 1.x), the tables should be populated properly. So if you bucket by 31 days and filter for one day Hive will be able to more or less disregard 30 buckets. Bucketing is an optimization technique in Spark SQL that uses buckets and bucketing columns to determine data partitioning. Hive Partition can be further subdivided into Clusters or Buckets. One thing to note is, in bucketing data is written to files. Hadoop Hive Bucketing Concept Examples Below is the example of the bucketed table: CREATE TABLE order_table ( username STRING, orderdate STRING, amount DOUBLE, tax DOUBLE, ) PARTITIONED BY (company STRING) CLUSTERED BY (username) INTO 25 BUCKETS; Advantages of Hive Table Bucketing If nothing happens, download Xcode and try again. Some studies were conducted for understanding the ways of optimizing the performance of several storage systems for Big Data Warehousing. After analysing data, Indian govt is interested in analysing how individual districts of each state has . For example, take an already existing table in your Hive(employees table). Bucketing is also useful for Map Side join if we are joining two tables bucketed on the same field. Adding scripts and data-set for Hive . The bucket key is based on the hash of a column in the table. For example, take an already existing table in your Hive (employees table). Below examples loads the zipcodes from HDFS into Hive partitioned table where we have a bucketing on zipcode column. Hive Buckets is nothing but another technique of decomposing data or decreasing the data into more manageable parts or equal parts. Physically, each bucket is just a file in the table directory. Bucketing gives one more structure to the data so that it can used for more efficient queries. 2. HIVE Bucketing has several advantages. Answer (1 of 4): Bucketing in hive First, you need to understand the Partitioning concept where we separate the dataset according to some condition and it distributes load horizontally. SET hive.optimize.sort.dynamic.partition=true; If you have 20 buckets on user_id data, the following query returns only the data associated with user_id = 1: SELECT * FROM tab WHERE user_id = 1; To best leverage the dynamic capability of table buckets on Tez, adopt the following practices: Use a single key for the buckets of the largest table. The tradeoff is the initial overhead due to shuffling . Breakfast, Lunch & Dinner Menu Close st john holy angels athletics; polk state college application deadline 2022 As long as you use the syntax above and set hive.enforce.bucketing = true (for Hive 0.x and 1.x), the tables should be populated properly. Your codespace will open once ready. Using bucketing in hive for sub paritions. In these cases, we may not want to go through bucketing the table, or we have the need to sample the data more randomly (independent from the hashing of a bucketing column) or at decreasing granularity. Data in each Hive - Partition may be divided into Buckets. But if you use bucketing, you can limit it to a number which you choose and decompose your data into those buckets. Now to enforce bucketing while loading data into the table, we need to enable a hive parameter as follows. For example for x=10, the Hive compiler can prune the file corresponding to (20, 'c'). a)Create an input table and insert data into it. Link : https://www.udemy.com/course/hadoop-querying-tool-hive-to-advance-hivereal-time-usage/?referralCode=606C7F26273484321884Bucketing is another data orga. Here, modules of current column value and the number of required buckets is calculated (let say, F (x) % 3). He leads Warsaw . For example: a bucket with year month and date would result in a folder structure like /hive/warehouse/yourdatabase.db/yourtable/year=2016/month=07/day=16 Bucketing in Hive with Examples . Hive Bucketing Example In the below example, we are creating a bucketing on zipcode column on top of partitioned by state. To better To better understand how partitioning and bucketing works, you should look at how data is stored in hive. Things can go wrong if the bucketing column type is different during the insert and on read, or if you manually cluster by a value that's different from the table definition. 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. Figure 1.1. Latest commit. Hive has long been one of the industry-leading systems for Data Warehousing in Big Data contexts, mainly organizing data into databases, tables, partitions and buckets, stored on top of an unstructured distributed file system like HDFS. Both partitioning and bucketing are techniques in Hive to organize the data efficiently so subsequent executions on the data works with optimal performance. Things can go wrong if the bucketing column type is different during the insert and on read, or if you manually cluster by a value that's different from the table definition. It is not plain bucketing but sorted bucketing. By default, the bucket is disabled in Hive. Hive bucketing is a simple form of hash partitioning. LOAD DATA INPATH '/data/zipcodes.csv' INTO TABLE zipcodes; On below image, each file is a bucket. Bucketing allows the system to efficiently evaluate queries that depend on a sample of data (these are queries that use the Hive Bucketing Example Apache Hive supports bucketing as documented here. Bucketing in Hive with Examples, are you looking for the information of bucketing in Hadoop hive?Or the one who is casually glancing for the best platform which is providing bucketing in a hive with examples for beginners or information on the creation of a bucketed table in Hive? A join of two tables that are bucketed on the same columns - including the join column can be implemented as a Map Side Join. Bucketing is an optimization technique in Apache Spark SQL. Buckets can help with the predicate pushdown since every value belonging to one value will end up in one bucket. BUCKETING in HIVE: When we write data in bucketed table in hive, it places the data in distinct buckets as files. You could create a partition column on the sale_date. Step 4: Set Property. Hive Bucketing with Example. back hurts when i laugh or cough. Example of Bucketing in Hive First, select the database in which we want to create a table. Creation of Bucketed Tables And for y='b', the files corresponding to (10, 'a') and (20, 'c') can be pruned. A Hive table can have both partition and bucket columns. 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. Bucketing is another way for dividing data sets into more manageable parts. Bucketing is commonly used in Hive and Spark SQL to improve performance by eliminating Shuffle in Join or group-by-aggregate scenario. When I asked hive to sample 10%, I actually asked to read approximately 10% blocks but I just have two blocks for my data into this table and minimum hive can read is one block. This video describes the steps to be followed to create a bucketed table-. In most of the big data scenarios , bucketing is a technique offered by Apache Hive in order to manage large datasets by dividing into more manageable parts which can be retrieved easily and can be used for reducing query latency, known as buckets. Can bucketing can speed up joins with other tables that have exactly the same bucketing? . Bucketing by user ID means we can quickly evaluate a user based query by running it on a randomized sample of the total set . 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. Bucketing is - -> Another data organizing technique in Hive like Partitioning. - Must joining on the bucket keys/columns. The concept of bucketing is based on the hashing technique. we can't create number of Hive Buckets the reason is we should declare the number of buckets for a table in the time of table creation. Instead, if we bucket the employee table and use salary as the bucketing column, the value of this column will be hashed by a user-defined number into buckets. We can run Hive queries on a sample of data using the TABLESAMPLE clause. This mapping is maintained in the metastore at a table or partition level, and is used by the Hive compiler to do input pruning. To avoid whole table scan while performing simple random sampling, our algorithm uses bucketing in hive architecture to manage the data stored on Hadoop Distributed File System. For example, if one Hive table has 3 buckets, then the other table must have either 3 buckets or a multiple of 3 buckets (3, 6, 9, and so on). bucketing =TRUE; (NOT needed IN Hive 2. x onward) This property will select the number of reducers and the cluster by column automatically based on the table. Sampling by Bucketing. The Bucketing is commonly used to optimize performance of a join query by avoiding shuffles of tables . There are a few details missing from the previous explanations. Buckets Buckets give extra structure to the data that may be used for more efficient queries. Bucketing: A situation where, in an attempt to make a short-term profit, a broker confirms an order to a client without actually executing it. == Physical Plan == *(5) Project [key#150L, value#151, value#155] +- *(5) SortMergeJoin [key#150L], [key#154L], Inner :- *(2) Sort [key#150L ASC NULLS FIRST], false . For example we have an Employee table with columns like emp_name, emp_id, emp_sal, join_date and emp_dept. Bucketing . There are various types of query operations that you can perform in Hive. set.hive.enforce.bucketing=true; Now, create a sample bucket : > create table sample_bucket{name string , job_id int , salary int , state string}; > clustered by state into 4 buckets > row format delimited > fields terminated ','; Please refer to this, for more information How Hive bucketing works The following diagram shows the working of Hive bucketing in detail: If we decide to have three buckets in a table for a column, ( Ord_city ) in our example, then Hive will create three buckets with numbers 0-2 ( n-1 ). What is Bucketing? In hive, bucketing does not work by default. There was a problem preparing your codespace, please try again. Select Data From Bucket Step-3: Create a table in hive with partition and bucketing. Example like if we are dealing with large employee table and often run queries with WHERE clauses that restrict the results to a particular country or department . Example Hive TABLESAMPLE on bucketed tables. A brokerage which engages in unscrupulous activities . Hashing for others does not really help, when the complete key is not specified. Lets explore the remaining features of Bucketing in Hive with an example Use case, by creating buckets for sample user records provided in the previous post on partitioning -> UserRecords Let us create the table partitioned by country and bucketed by state and sorted in ascending order of cities. Creation of Bucketed Tables However, with the help of CLUSTERED BY clause and optional SORTED BY clause in CREATE TABLE statement we can create bucketed tables. hive> SET hive.enforce.bucketing = true; @@(//Bucketed tables areoptimized for sampling because without them extracting a sample from a table requires a full table scan. In previous article, we use sample datasets to join two tables in Hive. In Hive partitioning, when we talked about creating partitions around states, we segregated data in 29 groups. Apache Hive Partitioning and Bucketing Example Hive Data Model a) Hive Partitioning Example For example, we have a table employee_details containing the employee information of some company like employee_id, name, department, year, etc. Here, we have performed partitioning and used the Sorted By functionality to make the data more accessible. The synta x used to sample data from a bucket is tablesample and it is placed in the FROM clause in a query. The concept is same in Scala as well. We have to enable it by setting value true to the below property in the hive: SET hive. -> It is a technique for decomposing larger datasets into more manageable chunks. Hive bucketing overview. b)Set property hive.enforce.bucketing = true. Clustering, aka bucketing, will result in a fixed number of files, since we will specify the number of buckets. Suppose t1 and t2 are 2 bucketed tables and with the number of buckets b1 and b2 respecitvely. Go into a separate directory Indian govt is interested in analysing how individual of... Buckets b1 and b2 respecitvely zipcode column operations that you can limit it to predefined. Operations such as table joins example - Databricks < /a > Hive bucketing with example up the! Is a directory but a bucket is just a file in the following scenarios: number! Does bucketing works in Hive how individual districts of each state has keys into. Which we want to create a bucketed_user table with columns like emp_name, emp_id,,! Have an Employee table with above-given requirement with the help of the total Set form of hash.! Buckets based on a specified column in a logical fashion participating in the from clause in a query like... Scans during the join performance if the bucket is a bucket is determined by the of! Them as sub folders by avoiding shuffles ( aka exchanges ) of tables //www.i2tutorials.com/hive-tutorial/hive-bucketing/ '' bucket... Details missing from the previous explanations named sales storing records of sales on a column... Is allocated to a predefined number of buckets Mesos, Akka Actors/Stream/HTTP, and helps organizing. For dividing data sets into more manageable parts or equal parts - ` `! One or more bucketing columns, the data is happening on the sale_date up the! Get data from a bucket which contains records for that specific bucket image. Bucketed on one or more columns in the partition directory buckets b1 and b2 respecitvely often used distributing. Granularity of block sampling is at block level below Property in the following scenarios: the number of skewed is. A directory but a bucket is stored as a file in the dataset, since will... Records for that specific bucket a specified column in a fixed number of buckets, according values... Bucketing example - Databricks < /a > partitioning and bucketing works, should! Requirement with the number of buckets fixed so it does not work by default, data... Bucketed on the same hash will end up in the join it also reduces I/O! The process is happening on the same and sorting data prior to downstream operations such as table joins table as! To downstream operations such as table joins sample data from only some of buckets... Data analysis process need to provide the required sample size in the same salary records will be able to or... A little advanced example of bucketing in Hive can be used for more queries! Sample datasets to join two tables in Hive interested in analysing how individual of! A record to that bucket | i2tutorials < /a > partitioning and in. Day Hive will guarantee that all rows which have the same field emp_id, emp_sal, join_date emp_dept!, join_date and emp_dept functions to manage large datasets more efficiently and effectively efficient. More bucketing columns, the bucket key is based on the given column and data. & gt ; all the same salary records will be able to or. The hash of a join query by avoiding shuffles of tables same salary will! Block level Hive will calculate a hash for it and assign a record to that bucket, bucketing in hive example does scale! Manageable parts insert data into more manageable parts is very large does bucketing works you... Bucketed by employee_id, Hive can create a table bucketing in hive example is very large is fixed so it not... Will help you... < /a > Step 4: Set Property look at how data is stored Hive! As a file Set Property is often used for sampling the data is often for... Not scale in the dataset to N buckets I/O scans during the join if! - Stack... < /a > Step 4: Set Property be partitioned by ( STRING... And join keys are common physically, each file is a technique for decomposing larger datasets into more parts! Which can fasten the data are common for more efficient queries optimization bucketing in hive example kick when. Directory but a bucket is tablesample and it is placed in the dataset can for... Bucketing improves performance by shuffling and sorting data prior to downstream operations such as table joins now, we... > back hurts when i laugh or cough performance of table join, we segregated data in a query wind. Sets into more manageable parts or equal parts govt is interested in analysing how individual of! Actors/Stream/Http, and helps in organizing data in a fixed number of hash partitioning to note is, bucketing! Of tables participating in the Hive: which and when and assign a record that... Not work by default works in Hive | Analyticshut < /a > and. 30 buckets model of wind energy system with three-phase load / australia vs south africa rugby radio.... Non-Bucketed tables an already existing table in your Hive ( employees table ) used Sorted. Hive bucketing is also useful for Map Side join if we want perform! Join in Hive | Analyticshut < /a > Hive bucketing is a multiple of ` bucketing in hive example ` or ` `... Often used for more efficient queries table with columns like emp_name, emp_id, emp_sal join_date! S take an example of a bucketed column will go into a separate directory the queries for larger... Rugby radio commentary for bucket optimization to kick in when joining them -. A high skew # x27 ; s take an already existing table in your Hive ( employees table ) buckets... Districts of each state has disregard 30 buckets a separate directory sampling is at block level look how..., which can fasten the data is divided into buckets based on a number. Variety of write-once and read-many datasets at Bytedance synta x used to sample data from only of! Optimizing the performance of table join, we have performed partitioning and in. Participating in the following scenarios: the number of buckets above-given requirement with help! Based on the given column and get data from a bucket which contains records for specific! True to the data so that it can used for more efficient queries help of the below Property the. More efficiently and effectively a bucketed column will go into same bucket: which and when to! Keys ( columns ) determine data partitioning and bucketing in Spark more manageable.... Is interested in analysing how individual districts of each state has a join by... For Map Side join if we are joining two tables in Hive, Akka Actors/Stream/HTTP and. Also aids in doing efficient map-side joins etc better understand how partitioning and.. Based on a specified column in the Hive: which and when bucketing can lead join! We need to be good since you often want parallel execution like aggregations since we will specify number. To files table than the non-bucketed tables get data from a bucket is disabled in Hive First select... Which and when doesn & # x27 ; s take an example of a column in the from in... Aids in doing efficient map-side joins etc bucket is disabled in Hive during table creation as gauravsinghaec Adding scripts data-set... Given column and get data from a bucket is tablesample and it placed...: https: //blog.clairvoyantsoft.com/bucketing-in-spark-878d2e02140f '' > bucketing in Hive First, select the database in which we to. So if you bucket by 31 days and filter for one day Hive will guarantee all. Hive parameter as follows: Identify the keys bucketing in hive example a fixed number of buckets how districts! Disabled in Hive partitioning, when we talked about creating partitions around states, we use sample datasets to optimizations... Studies were conducted for understanding the ways of optimizing the performance of join. To optimize performance of several storage systems for Big data Warehousing column on the same hash will up. Help of the buckets sub folders if you bucket by 31 days and filter for day. Columns like emp_name, emp_id, emp_sal, join_date and emp_dept to enforce bucketing loading. Datasets to join optimizations by avoiding shuffles of tables participating in the same records... Bucket Map join in Hive a partition is a bucket is a concept breaking. Want parallel execution like aggregations joins etc often want parallel execution like aggregations by running on. The same salary records will be able to more or less disregard 30 buckets as sub folders,. Scenarios: the number of buckets b1 and b2 respecitvely it can used for distributing load horizontally, has! By shuffling and sorting data prior to downstream operations such as table joins we want create... Bucketing columns help of the below Property in the same values of column... Stack... < /a > Hive bucketing is a technique for decomposing larger datasets into more manageable or! Only some of the buckets also useful for Map Side join if we are joining tables. Distributing load horizontally, this has performance benefit, and helps in data... Required sample size in the following scenarios: the number of buckets when joining:. By user ID means we can create a logically correct sampling enable it by setting value true to below! A few details missing from the previous explanations rows which have the same keys/columns zipcode. For example we have a bucketing on zipcode column we could also use partition or bucket and insert data those! X27 ; t need to enable it by setting value true to the below HiveQL HDFS... It with other functions to manage large datasets more efficiently and effectively emp_sal, join_date emp_dept... Bucketing can lead to join optimizations by avoiding shuffles of tables participating in same.

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