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Paper Reading - Resilient Distributed Datasets

17 Jun 2022

In this post we’ll discuss the paper Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing by Zaharia et al [1]. In a nutshell, Resilient Distributed Datasets (RDDs for short) is the main abstraction used by the Spark query engine.

Resilient Distributed Datasets


RDD is a read-only, collection of records grouped in partitions. It can represent data in storage (such as HDFS) but also the result of transformations (such as map, filter and joins) of other RDDs.

Users can control how RDDs are partitioned via a partitioner.

Lineage Graph

RDDs are lazily evaluated. It can be derived from other RDDs by storing this information via a lineage graph.

Storing the lineage is also useful for recovery. For example, if one of the partitions of a RDD fails, it can be use its lineage graph to recompute only the partition that failed.

A child node might depend on multiple parent nodes, for example through a union of multiple RDDs. Conversely a node might be a dependency of multiple children. If a node is a dependency of a single child node, we call it a narrow dependency, otherwise we call it a wide dependency. Figure 1 shows some examples.

Diagram with 5 examples of dependencies. Each example contains blocks with smaller blocks representing partitions. Partitions on the right blocks depend on partitions on the left blocks (connected by an arrow).
Figure 1: Narrow vs. Wide dependencies (source: [1])

A narrow dependency is desirable for optimization because the parent node can then be potentially co-located with the child, we’ll see in more details in Scheduling.


Users can indicate that a RDD is kept in memory. It can then be shared between multiple downstream RDDs to speed up computation. If the data is too big for memory it might instead be persisted in disk.

Scala API

The Spark framework provides a Scala API used to manipulate RDDs. For example, if we want to read from HDFS and filter lines starting with ERROR, we can do:

lines = spark.textFile("hdfs://...")
errors = lines.filter(_.startsWith("ERROR"))

Behind the scenes, a RDD is created for lines. When persist() is called, it tells the RDD to be kept in memory, indicating it will be used later. So far, no work has been done, only the computation graph is being constructed.

Next, we can call count(), which will cause the computation to be evaluated and returned to the local machine.


We can then do further processing on errors. The example below filters lines containing HDFS, retrieves the 4-th column and returns the list to the user, forcing another evaluation.


Since errors is cached in memory (due to .persist()), we won’t be reading data from HDFS again.

Implementation Details

A RDD is represented by a class implementing an interface, with the following core methods [1]:

Operation Meaning
partitions() Return a list of Partition objects
preferredLocations(p) List nodes where partition p can be accessed faster due to data locality
dependencies() Return a list of the parents
iterator(p, ppIt) Compute the elements of partition p given iterators for its parent partitions ppIt
partitioner() Return metadata specifying whether the RDD is hash/range partitioned

Some examples make it easier to understand.

HDFS Files

For the RDD representing HDFS files:


Applying the map(f: T -> U) function over a RDD (the parent) creates another RDD:


The join operation is only allowed between RDDs whose internal type is a pair of key-value and it does the join based on matching keys.

Let’s consider a simple example: joining between two datasets keyed on some ID, supposing both datasets are partitioned the same way by ID. The joined RDD can inherit the partition scheme (the partitioner) from either parent, so its partitions will also be based on the ID column.

A more complex case is if the parent datasets are not partitioned the same way, in which case the resulting RDD has to pick one of the parent’s partitioner or come up with its own.


Once we execute one of the “evaluation” functions like count(), the scheduler will construct a DAG of stages to execute based on the lineage graph of the RDD.

It tries to co-locate the narrow dependencies together and for the wide dependencies it performs shuffling like in MapReduce [2]. For shuffling, it persists the parents’ partitions to disk to make fault recovery simpler.

It also takes into account cached data, if tries to colocate computation in the node that holds the cache data. Otherwise the scheduler will leverage the preferredLocations() from the RDDs.

Figure 2 shows an example of the arrangement of stages for a given Spark job.

Diagram representing job stages, RDDs and their dependencies.
Figure 2: Example of how Spark computes job stages. Boxes with solid outlines are RDDs. Partitions are blue rectangles, in black if they are already in memory. To run an action on RDD G, we build build stages at wide dependencies and pipeline narrow transformations inside each stage. In this case, stage 1’s output RDD is already in RAM, so we run stage 2 and then 3 (source: [1])

If a node fails, it will recompute the work in another node. In case the parent’s data is not persisted, it will recompute the dependencies.

Comparison with FlumeJava

I find it appropriate to compare RDD/Spark and FlumeJava, considering we discussed the latter in a recent post.

Topic FlumeJava Spark
API Language Java Scala
Unit of abstraction MSCR RDD
Evaluation Explicit via .run() Implicit via calls like .count()
Underlying Framework MapReduce Mesos
Recovery Mechanism Checkpoint (between MapReduce jobs) Mix of checkpoint, cache and recomputation
Cache Disk (distributed filesystem) Memory/Disk
Optimization ParallelDo Fusion, Sink Flattening Narrow Dependency Co-location


Spark’s architecture is, surprisingly, relatively simple! I don’t recall where I heard this but it was something along the lines “In distributed systems, simple is better”. This is obviously applicable to software in general, but distributed systems have inherent complexity so that the tradeoff between simplicity vs. say, efficiency, is different than in a single node.

I also found RDDs similar to FlumeJava in many aspects. The major gains enabled by RDDs seems to be from opening the MapReduce black box for a tighter integration with the overall system.