What is Deno?
One of the most frequently used transformations in Apache Spark is Join operation. Joins in Apache Spark allow the developer to combine two or more data frames based on certain (sortable) keys. The syntax for writing a join operation is simple but some times what goes on behind the curtain is lost. Internally, for Joins Apache Spark proposes a couple of Algorithms and then chooses one of them. Not knowing what these internal algorithms are, and which one does spark choose might make a simple Join operation expensive.
While opting for a Join Algorithm, Spark looks at the size of the data frames involved. It considers the Join type and condition specified, and hint (if any) to finally decide upon the algorithm to use. In most of the cases, Sort Merge join and Shuffle Hash join are the two major power horses that drive the Spark SQL joins. But if spark finds the size of one of the data frames less than a certain threshold, Spark puts up Broadcast Join as it’s top contender.
Looking at the Physical plan of a Join operation, a Broadcast Hash Join in Spark looks like this
The above plan shows that the data frame from one of the branches broadcasts to every node containing the other data frame. In each node, Spark then performs the final Join operation. This is Spark’s per-node communication strategy.
Spark uses the Broadcast Hash Join when one of the data frame’s size is less than the threshold set in spark.sql.autoBroadcastJoinThreshold. It’s default value is 10 Mb, but can be changed using the following code
spark.conf.set("spark.sql.autoBroadcastJoinThreshold", 100 * 1024 * 1024)
This algorithm has the advantage that the other side of the join doesn’t require any shuffle. If this other side is very large, not doing the shuffle will bring notable speed-up as compared to other algorithms that would have to do the shuffle.
Broadcasting large datasets can also lead to timeout errors. A configuration spark.sql.broadcastTimeout sets the maximum time that a broadcast operation should take, past which the operation fails. The default timeout value is 5 minutes, but it can be set as follows:
If neither of the data frames can be broadcasted, then Spark resorts to Sort Merge Join. This algorithm uses the node-node communication strategy, where Spark shuffles the data across the cluster.
Sort Merge Join requires both sides of the join to have correct partitioning and order. Generally, this is ensured by** shuffle and sort** in both branches of the join as depicted below
#apache spark #scala #tech blogs #broadcast join #join opertaions #join optimization #joins in spark #shuffled hash join #sort merge join
CROSS JOIN is in the spotlight. This article finishes our small series of SQL JOIN-related publications.
SQL Server CROSS JOIN is the simplest of all joins. It implements a combination of 2 tables without a join condition. If you have 5 rows in one table and 3 rows in another, you get 15 combinations. Another definition is a Cartesian Product.
Now, why would you want to combine tables without a join condition? Hang on a bit because we are getting there. First, let’s refer to the syntax.
#sql server #cross join #inner join #outer join #sql join #sql
Outer join is at the center stage today. And this is part 2 of your ultimate guide to SQL joins. If you missed part 1, here’s the link.
By the looks of it, outer is the opposite of inner. However, if you consider the outer join this way, you’ll be confused. To top that, you don’t have to include the word outer in your syntax explicitly. It’s optional!
But before we dive in, let’s discuss nulls concerning outer joins.
When you join 2 tables, one of the values from either table can be null. For INNER JOINs, records with nulls won’t match, and they will be discarded and won’t appear in the result set. If you want to get the records that don’t match, your only option is OUTER JOIN.
Going back to antonyms, isn’t that the opposite of INNER JOINs? Not entirely, as you will see in the next section.
#sql server #inner join #outer join #sql join #sql
What is Deno?
Hey youtube, This is Prince and welcome back to another exciting video and in this video we are going to explore a bit of Deno JS !
Comparison : 1:39
Installation : 3:39
#deno #deno js