How to perform vlook up in spark rdd

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lookup operation in spark

I have two rdd

rdd1 =[('1', 3428), ('2', 2991), ('3', 2990), ('4', 2883), ('5', 2672), ('5', 2653)]
rdd2 = [['1', 'Toy Story (1995)'], ['2', 'Jumanji (1995)'], ['3', 'Grumpier Old Men (1995)']]

I want to perform an operation to relace first rdd's first element with second rdd's second element

My final result will be like this

[(''Toy Story (1995)'', 3428), ('Jumanji (1995)', 2991), ('Grumpier Old Men (1995)', 2990)]

Please refer me a way to perform this

Use join and map:

rdd1.join(rdd2).map(lambda x: (x[1][1], x[1][0])).collect()
#[('Toy Story (1995)', 3428),
# ('Jumanji (1995)', 2991),
# ('Grumpier Old Men (1995)', 2990)]

Lookup additional data in Spark Streaming, By means of initialState(), an RDD can be passed as an initial state. However, any updates can only be performed based on a key. This also� Generic function to combine the elements for each key using a custom set of aggregation functions. Turns an RDD[(K, V)] into a result of type RDD[(K, C)], for a "combined type" C Note that V and C can be different -- for example, one might group an RDD of type (Int, Int) into an RDD of type (Int, Seq[Int]).

You can use a list comprehension for this:

>>> [(y[1], x[1]) for x in rdd1 for y in rdd2 if x[0] == y[0]]
[('Toy Story (1995)', 3428),
 ('Jumanji (1995)', 2991),
 ('Grumpier Old Men (1995)', 2990)]

PySpark Cheat Sheet: Spark in Python, This PySpark cheat sheet covers the basics, from initializing Spark and loading your data, to retrieving RDD information, sorting, filtering and� An RDD in Spark can be cached and used again for future transformations, which is a huge benefit for users. RDDs are said to be lazily evaluated, i.e., they delay the evaluation until it is really needed. This saves a lot of time and improves efficiency. Features of an RDD in Spark

You can do it using Broadcast and Dataframe operations if working on large data on a cluster for performance gains

df_points = spark.createDataFrame(rdd1, schema=['index', 'points'])
df_movie = spark.createDataFrame(rdd2, schema=['index', 'Movie'])
df_join = df_points.join(broadcast(df_movie), on='index').select("Movie","points")

You can also convert back to RDD if needed

df_join.rdd.map(list).collect()

lookup an rdd for values of another rdd, lookup an rdd for values of another rdd. sparkrddscala sparkscala spark mllib. Question by Nandita Dwivedi � May 11, 2017 at 12:54 PM �. I have a lookup rdd of � Resilient Distributed Datasets (RDDs) are the core concepts in Spark. In order to understand how spark works, we should know what RDD’s are and how they work. The Spark RDD is a fault tolerant, distributed collection of data that can be operated in parallel. Each RDD is split into multiple partitions, and spark runs one task for each partition.

Python Language Knowledge Base, How do you perform basic joins of two RDD tables in Spark using Python? What is the syntax using python on spark for: Inner Join Left Outer Join Cross Join With two tables (RDD) with a single column in vlookup in Pandas using join. First, this work is wonderful, your work is absolutely amazing! If I want to lookup a certain pair by given key, what method could I use? If there's not, in which file I can write one? Thanks!

Using a pandas dataframe as a lookup table, Two-Dimensional VLOOKUP in Pandas, Question: Given two tables as shown below. title. Performing lookup/translation in a Spark RDD or data frame using � Apache Spark RDDs are a core abstraction of Spark which is immutable. In this blog, we will discuss a brief introduction of Spark RDD, RDD Features-Coarse-grained Operations, Lazy Evaluations, In-Memory, Partitioned, RDD operations- transformation & action RDD limitations & Operations.

RDD Programming Guide, Initializing Spark. Scala; Java; Python. The first thing a Spark program must do is to create a SparkContext object, which tells Spark� In Spark, the distributed datasets can be created from any type of storage sources supported by Hadoop such as HDFS, Cassandra, HBase and even our local file system. Spark provides the support for text files, SequenceFiles, and other types of Hadoop InputFormat. SparkContext's textFile method can be used to create RDD's text file. This method