What is RDD in spark

问题:

Definition says:

RDD is immutable distributed collection of objects

I don't quite understand what does it mean. Is it like data (partitioned objects) stored on hard disk If so then how come RDD's can have user-defined classes (Such as java, scala or python)

From this link: https://www.safaribooksonline.com/library/view/learning-spark/9781449359034/ch03.html It mentions:

Users create RDDs in two ways: by loading an external dataset, or by distributing a collection of objects (e.g., a list or set) in their driver program

I am really confused understanding RDD in general and in relation to spark and hadoop.

Can some one please help.

回答1:

An RDD is, essentially, the Spark representation of a set of data, spread across multiple machines, with APIs to let you act on it. An RDD could come from any datasource, e.g. text files, a database via JDBC, etc.

The formal definition is:

RDDs are fault-tolerant, parallel data structures that let users explicitly persist intermediate results in memory, control their partitioning to optimize data placement, and manipulate them using a rich set of operators.

If you want the full details on what an RDD is, read one of the core Spark academic papers, Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing

回答2:

RDD is a logical reference of a dataset which is partitioned across many server machines in the cluster. RDDs are Immutable and are self recovered in case of failure.

dataset could be the data loaded externally by the user. It could be a json file, csv file or a text file with no specific data structure.

UPDATE: Here is the paper what describe RDD internals:

Hope this helps.

回答3:

Formally, an RDD is a read-only, partitioned collection of records. RDDs can only be created through deterministic operations on either (1) data in stable storage or (2) other RDDs.

RDDs have the following properties –

1. Immutability and partitioning: RDDs composed of collection of records which are partitioned. Partition is basic unit of parallelism in a RDD, and each partition is one logical division of data which is immutable and created through some transformations on existing partitions.Immutability helps to achieve consistency in computations.

   Users can define their own criteria for partitioning based on keys on which they want to join multiple datasets if needed.

2. Coarse grained operations: Coarse grained operations are operations which are applied to all elements in datasets. For example – a map, or filter or groupBy operation which will be performed on all elements in a partition of RDD.

3. Fault Tolerance: Since RDDs are created over a set of transformations , it logs those transformations, rather than actual data.Graph of these transformations to produce one RDD is called as Lineage Graph.

For example –

firstRDD=sc.textFile("hdfs://...")

secondRDD=firstRDD.filter(someFunction);

thirdRDD = secondRDD.map(someFunction);

result = thirdRDD.count()

In case of we lose some partition of RDD , we can replay the transformation on that partition in lineage to achieve the same computation, rather than doing data replication across multiple nodes.This characteristic is biggest benefit of RDD , because it saves a lot of efforts in data management and replication and thus achieves faster computations.

4. Lazy evaluations: Spark computes RDDs lazily the first time they are used in an action, so that it can pipeline transformations. So , in above example RDD will be evaluated only when count() action is invoked.

5. Persistence: Users can indicate which RDDs they will reuse and choose a storage strategy for them (e.g., in-memory storage or on Disk etc.)

These properties of RDDs make them useful for fast computations.

回答4:

Resilient Distributed Dataset (RDD) is the way Spark represents data. The data can come from various sources :

• Text File
• CSV File
• JSON File
• Database (via JBDC driver)

RDD in relation to Spark

Spark is simply an implementation of RDD.

RDD in relation to Hadoop

The power of Hadoop reside in the fact that it let users write parallel computations without having to worry about work distribution and fault tolerance. However, Hadoop is inefficient for the applications that reuse intermediate results. For example, iterative machine learning algorithms, such as PageRank, K-means clustering and logistic regression, reuse intermediate results.

RDD allows to store intermediate results inside the RAM. Hadoop would have to write it to an external stable storage system, which generate disk I/O and serialization. With RDD, Spark is up to 20X faster than Hadoop for iterative applications.

Futher implementations details about Spark

Coarse-Grained transformations

The transformations applied to an RDD are Coarse-Grained. This means that the operations on a RDD are applied to the whole dataset, not on its individual elements. Therefore, operations like map, filter, group, reduce are allowed, but operations like set(i) and get(i) are not.

The inverse of coarse-grained is fine-grained. A fine-grained storage system would be a database.

Fault Tolerant

RDD are fault tolerant, which is a property that enable the system to continue working properly in the event of the failure of one of its components.

The fault tolerance of Spark is strongly linked to its coarse-grained nature. The only-way to implement fault tolerance in a fine-grained storage system is to replicate its data or log updates across machines. However, in a coarse-grained system like Spark, only the transformations are logged. If a partition of an RDD is lost, the RDD has enough information the recompute it quickly.

Data storage

The RDD is "distributed" (separated) in partitions. Each partitions can be present in the memory or on the disk of a machine. When Spark wants to launch a task on a partition, he sends it to the machine containing the partition. This is know as "locally aware scheduling".

Sources : Great research papers about Spark : http://spark.apache.org/research.html

Include the paper suggested by Ewan Leith.

回答5:

RDD = Resilient Distributed Dataset

Resilient (Dictionary meaning) = (of a substance or object) able to recoil or spring back into shape after bending, stretching, or being compressed

RDD is defined as (from LearningSpark - OREILLY): The ability to always recompute an RDD is actually why RDDs are called “resilient.” When a machine holding RDD data fails, Spark uses this ability to recompute the missing partitions, transparent to the user.

This means 'data' is surely available at all times. Also, Spark can run without Hadoop and hence data is NOT replicated. One of the best characterstics of Hadoop2.0 is 'High Availbility' with the help of Passive Standby Namenode. The same is achieved by RDD in Spark.

A given RDD (Data) can span across various nodes in Spark cluster (like in Hadoop based cluster).

If any node crashes, Spark can re-compute the RDD and loads the data in some other node, and data is always available. Spark revolves around the concept of a resilient distributed dataset (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel (http://spark.apache.org/docs/latest/programming-guide.html#resilient-distributed-datasets-rdds)

回答6:

To compare RDD with scala collection, below are few differences

1. Same but runs on a cluster
2. Lazy in nature where scala collections are strict
3. RDD is always Immutable i.e., you can not change the state of the data in the collection
4. RDD are self recovered i.e., fault-tolerant

回答7:

RDD (Resilient Distributed Datasets) are an abstraction for representing data. Formally they are a read-only, partitioned collection of records that provides a convenient API.

RDD provide a performant solution for processing large datasets on cluster computing frameworks such as MapReduce by addressing some key issues:

• data is kept in memory to reduce disk I/O; this is particularly relevant for iterative computations -- not having to persist intermediate data to disk
• fault-tolerance (resilience) is obtained not by replicating data but by keeping track of all transformations applied to the initial dataset (the lineage). This way, in case of failure lost data can always be recomputed from its lineage and avoiding data replication again reduces storage overhead
• lazy evaluation, i.e. computations are carried out first when they're needed

RDD's have two main limitations:

• they're immutable (read-only)
• they only allow coarse-grained transformations (i.e. operations that apply to the entire dataset)

One nice conceptual advantage of RDD's is that they pack together data and code making it easier to reuse data pipelines.

Sources: Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing, An Architecture for Fast and General Data Processing on Large Clusters

回答8:

RDD is a way of representing data in spark.The source of data can be JSON,CSV textfile or some other source. RDD is fault tolerant which means that it stores data on multiple locations(i.e the data is stored in distributed form ) so if a node fails the data can be recovered. In RDD data is available at all times. However RDD are slow and hard to code hence outdated. It has been replaced by concept of DataFrame and Dataset.