Sage-Tips

How does join work in MapReduce?

Once a join in MapReduce is distributed, either Mapper or Reducer uses the smaller dataset to perform a lookup for matching records from the large dataset and then combine those records to form output records.

What are joins in hive in MapReduce paradigm?

Hive joins are executed by MapReduce jobs through different execution engines like for example Tez, Spark or MapReduce. Joins even of multiple tables can be achieved by one job only. Since it’s first release many optimizations have been added to Hive giving users various options for query improvements of joins.

How do you implement MAP Side join in hive?

The syntax for Map Join in Hive. If we want to perform a join query using map-join then we have to specify a keyword “/*+ MAPJOIN(b) */” in the statement as below: SELECT /*+ MAPJOIN(c) */ * FROM tablename1 t1 JOIN tablename2 t2 ON (t1. emp_id = t2. emp_id);

How do you implement joins in hive?

How to Perform Joins in Apache Hive

1. INNER JOIN – Select records that have matching values in both tables.
2. LEFT JOIN (LEFT OUTER JOIN) – Returns all the values from the left table, plus the matched values from the right table, or NULL in case of no matching join predicate.

What is a reduce side join?

What is Reduce Side Join? As discussed earlier, the reduce side join is a process where the join operation is performed in the reducer phase. Basically, the reduce side join takes place in the following manner: Mapper reads the input data which are to be combined based on common column or join key.

What do you mean by map side join and reduce side join in MapReduce?

In Map-side join, all the task to join the records will be done by the mapper. This type of join is suitable for small sized tables. In Reduce-side join, the join task will be done by the reducer.

How do you optimize a join in Hive?

Physical Optimizations:

1. Partition Pruning.
2. Scan pruning based on partitions and bucketing.
3. Scan pruning if a query is based on sampling.
4. Apply Group By on the map side in some cases.
5. Optimize Union so that union can be performed on map side only.
6. Decide which table to stream last, based on user hint, in a multiway join.

What is map side join and reduce side join Hive?

What are the advantages of using map side join in MapReduce?

Advantages of using map side join:

• Map-side join helps in minimizing the cost that is incurred for sorting and merging in the shuffle and reduce stages.
• Map-side join also helps in improving the performance of the task by decreasing the time to finish the task.

Which is faster map side join or reduce side join Why?

Map side join is usually used when one data set is large and the other data set is small. Whereas the Reduce side join can join both the large data sets. The Map side join is faster as it does not have to wait for all mappers to complete as in case of reducer. Hence reduce side join is slower.

What is Hive join?

Basically, for combining specific fields from two tables by using values common to each one we use Hive JOIN clause. In other words, to combine records from two or more tables in the database we use JOIN clause. However, it is more or less similar to SQL JOIN. Also, we use it to combine rows from multiple tables.

When to Use map side join and reduce side join?

The Map side join and the reduce side join. Map side join is usually used when one data set is large and the other data set is small. Whereas the Reduce side join can join both the large data sets. The Map side join is faster as it does not have to wait for all mappers to complete as in case of reducer.

Why do we use map side join in hive?

Also, we use Hive Map Side Join since one of the tables in the join is a small table and can be loaded into memory. So that a join could be performed within a mapper without using a Map/Reduce step. Although even if queries frequently depend on small table joins, usage of map joins speed up queries’ execution.

How do hive map/reduce jobs work?

All five tables are joined in a single map/reduce job and the values for a particular value of the key for tables b, c,d, and e are buffered in the memory in the reducers. Then for each row retrieved from a, the join is computed with the buffered rows. If the STREAMTABLE hint is omitted, Hive streams the rightmost table in the join.

How to speed up the hive queries?

Hence, to speed up the Hive queries, we can use Map Join in Hive. Also, we use Hive Map Side Join since one of the tables in the join is a small table and can be loaded into memory. So that a join could be performed within a mapper without using a Map/Reduce step.

How do hive language manual joins work?

Adapted from : https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Joins : All five tables are joined in a single map/reduce job and the values for a particular value of the key for tables b, c,d, and e are buffered in the memory in the reducers. Then for each row retrieved from a, the join is computed with the buffered rows.