This is a very simple question: in spark, broadcast
can be used to send variables to executors efficiently. How does this work ?
More precisely:
broadcast
, or when the values are used ?.value
method ?Broadcast variables in Apache Spark is a mechanism for sharing variables across executors that are meant to be read-only. Without broadcast variables these variables would be shipped to each executor for every transformation and action, and this can cause network overhead.
The maximum size for the broadcast table is 8GB. Spark also internally maintains a threshold of the table size to automatically apply broadcast joins. The threshold can be configured using spark.
Spark supports two types of shared variables: broadcast variables, which can be used to cache a value in memory on all nodes, and accumulators, which are variables that are only “added” to, such as counters and sums. This guide shows each of these features in each of Spark's supported languages.
Broadcast join in spark is preferred when we want to join one small data frame with the large one. the requirement here is we should be able to store the small data frame easily in the memory so that we can join them with the large data frame in order to boost the performance of the join.
This is a very simple question: in spark, broadcast can be used to send variables to executors efficiently. How does this work ? when are values sent : as soon as I call broadcast, or when the values are used ?
Broadcast Hash Join in Spark works by broadcasting the small dataset to all the executors and once the data is broadcasted a standard hash join is performed in all the executors. Broadcast Hash Join happens in 2 phases. Hash Join phase – small dataset is hashed in all the executors and joined with the partitioned big dataset.
DataFrames up to 2GB can be broadcasted so a data file with tens or even hundreds of thousands of rows is a broadcast candidate. Broadcast joins are a powerful technique to have in your Apache Spark toolkit.
Spark broadcasts the common data (reusable) needed by tasks within each stage. The broadcasted data is cache in serialized format and deserialized before executing each task. You should be creating and using broadcast variables for data that shared across multiple stages and tasks.
sc.broadcast(variable)
is called.The answer is in Spark's source, in TorrentBroadcast.scala
.
When sc.broadcast
is called, a new TorrentBroadcast
object is instantiated from BroadcastFactory.scala
. The following happens in writeBlocks()
, which is called when the TorrentBroadcast object is initialized:
MEMORY_AND_DISK
policy.When new executors are created, they only have the lightweight TorrentBroadcast
object, that only contains the broadcast object's identifier, and its number of blocks.
The TorrentBroadcast
object has a lazy[2] property that contains its value. When the value
method is called, this lazy property is returned. So the first time this value function is called on a task, the following happens:
getRemoteBytes
is called on the block manager to fetch them. Network traffic happens only at that time.MEMORY_AND_DISK_SER
.[0] Compressed with lz4 by default. This can be tuned.
[1] The blocks are stored in the local block manager, using MEMORY_AND_DISK_SER
, which means that it spills partitions that don't fit in memory to disk. Each block has an unique identifier, computed from the identifier of the broadcast variable, and its offset. The size of blocks can be configured; it is 4Mb by default.
[2] A lazy val in scala is a variable whose value is evaluated the first time it is accessed, and then cached. See the documentation.
it:
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