The document discusses the core components and advantages of Apache Spark, particularly focusing on Resilient Distributed Datasets (RDDs) and their fault tolerance capabilities. It highlights Spark's architecture compared to in-memory data grids, emphasizing the benefits of immutability, partitioning, and efficient transformations. Furthermore, it introduces future developments such as Project Tungsten and DataFrames aimed at enhancing performance and code efficiency.

1. Why Spark
Secrets of Apache Spark’s Success

2. This Talk
● RDDs
● Spark vs InMemory Data Grids (IMDG)
● Programming model
● Reuse - DRY
● Higher level abstraction
● Scala
● Interactive Shells
● Other
● Future

3. About Me
● Solution Architect/Dev Manager/Developer/Market Risk
SME at a tier 1 investment bank
● 20 years of JVM experience
● 2011 - Hadoop + Map Reduce
● 2012 - Hive, then Shark
● 2013 - Spark, Scala, Play and Spray
● 2014 - Spark Streaming, Spark as a compute grid,
Spark ML
● 2015 - Independent Apache Spark consultant

4. Map Reduce
● Good
○ High level abstraction (Map and Reduce)
○ Distribution and fault tolerance
● Not so Good
● Lack abstractions for leveraging distributed memory
● Not efficient for iterative algorithms and interactive
data mining (SQL)

5. Solution - use shared memory
Challenges
● not abstracted for general use
● fault tolerant and resiliency

6. Existing In memory solutions
● Distributed shared memory (Coherence, key
value stores, database, etc)
● Allow fine grained updated to mutable state
● Fault tolerance hard to achieve - requires
replication, logging and checkpointing
● network bandwidth < memory bandwidth
● substantial storage overheads

7. Spark RDDs - what’s different
● RDD is a read-only, partitioned collection of records
● Interface based on coarse grained transformations
(map, filter and join)
● Fault tolerance using lineage rather than actual data
● if a partition is lost, the RDD has enough information to
recreate it from other RDDs to recompute the partition
without requiring replication
● Immutable RDDs

8. Spark - what’s different from IMDGs
. Property RDDs IM Data Grids
Reads Coarse- or fine-grained Fine-grained
Writes Coarse-grained Fine-grained
Fault recovery Fine-grained and low
overhead using lineage
Requires checkpoints and
program rollback
Straggler mitigation Possible using backup tasks Difficult
Work placement Automatic based on data
locality
Up to app (runtimes aim for
transparency)
Behavior if not enough RAM Similar to existing data flow
systems
Poor performance
(swapping?)

9. RDDs - what’s different
● only the lost partitions of an RDD need to be
recomputed upon failure, and they can be
recomputed in parallel on different nodes,
without having to roll back the whole
program.

10. RDDs - Straggler Migration
● A second benefit of RDDs is that their
immutable nature lets a system mitigate slow
nodes (stragglers) by running backup copies
of slow tasks as in MapReduce. Backup
tasks would be hard to implement with DSM,
as the two copies of a task would access the
same memory locations and interfere with
each other’s updates..

11. RDD Representation
● Set of partitions (“splits”)
● List of dependencies on parent RDDs
○ narrow, e.g. map, filter
○ wide, e.g. groupBy, require shuffle
● Function to compute a partition given parents
● Optional preferred locations
● Optional partitioning information

12. Hadoop RDD
partitions : one per HDFS block
dependencies : none
compute(partitions) : read corresponding block
preferred locations : HDFS block locations
partitioner : none

13. Filtered RDD
partitions : same as parent RDD
dependencies : “one-to-one” on parent
compute(partition) : compute parent and filter it
preferred locations(part) : none (ask parent)
partitioner = none

14. Joined RDD
partitions : one per reduce task
dependencies : shuffle on each parent
compute(partition) : read and join shuffled data
preferred locations(part) : none
partitioner = HashPartitioner(numTasks)

15. RDDs - Memory not essential
RDDs degrade gracefully when there is not
enough memory to store them, as long as they
are only being used in scan-based operations.
Partitions that do not fit in RAM can be stored
on disk and will provide similar performance to
current data-parallel systems.

16. RDDs - generic abstraction
● Coarse grained transformation only are a good fit for
many parallel applications
● RDDs efficiently express many programming models -
Map Reduce, SQL, Graph, MLLib
● many parallel programs naturally apply the same
operation to many records, making them easy to
express
● immutability of RDDs is not an obstacle because one
can create multiple RDDs to represent versions of the
same dataset

19. RDDs - persistence and partitioning
● Users can control two other aspects of RDDs:
persistence and partitioning. Users can indicate which
RDDs they will reuse and choose a storage strategy for
them (e.g., in-memory storage). They can also ask that
an RDD’s elements be partitioned across machines
based on a key in each record. This is useful for
placement optimizations, such as ensuring that two
datasets that will be joined together are hash-partitioned
in the same way

20. ● inspects RDD’s lineage graph to build a DAG of stages
to execute. Each stage contains as many pipelined
transformations with narrow dependencies as possible.
● The boundaries of the stages are the shuffle operations
required for wide dependencies, or any already
computed partitions that can shortcircuit the
computation of a parent RDD. The scheduler then
launches tasks to compute missing partitions from each
stage until it has computed the target RDD.
● Cached RDDs not recomputed.
Scheduler

22. Dont reinvent the wheel
● reuse Hadoop APIs - InputOutput formats,
codecs
● Hive QL and data types (Serdes)
● Hive Server
● Spark’s scheduler uses our representation of
RDDs, making it fault tolerant and scalable
● Productivity - Spark Shell

23. ● Compatible with JVM ecosystem. Massive
legacy codebase in big data
● DSL support Newer Spark API’s are
effectively DSL’s
● Concise syntax
● Rapid prototyping, but still type safe
● Thinking functionally Encourages
immutability and good practices
Written in Scala

24. ● Smart team
● Dont bite more than what you can chew
(Spark Core, ML + SQL, Streaming next)
● Open
● Community
● Process driven - build automation, test
coverage, api compatibility checks
Other secrets

25. Don’t use Spark when you need -
● asynchronous finegrained updates to shared
state, such as a storage system for a web
application or an incremental web crawler.
For these applications, it is more efficient to
use systems that perform traditional update
logging and data checkpointing, such as
databases

26. More to come - Project Tungsten
● Project Tungsten (overcome JVM limitations)
○ Memory management and binary processing leveraging application
semantics to manage memory explicitly and eliminate the overhead of
JVM object model and garbage collection
○ Cache-aware computation: algorithms and data structures to exploit
memory hierarchy
○ Code generation: using code generation to exploit modern compilers
and CPUs
● Data Frames
○ write less code
○ read less data (predicate push down)

27. More to come - Data Frames
● write less code
● read less data
○ convert to efficient formats
○ columnar formats
○ use partitioning
○ skip data using statistics
○ predicate pushdown
● let the optimiser (Catalyst) do the hard work