This page contains a bunch of spark pipeline transformation methods, whichwe can use for different problems. Use this as a quick cheat on how we cando particular operation on spark dataframe or pyspark.
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Read the partitioned json files from disk
applicable to all types of files supported
Save partitioned files into a single file.
Here we are merging all the partitions into one file and dumping it intothe disk, this happens at the driver node, so be careful with sie ofdata set that you are dealing with. Otherwise, the driver node may go out of memory.
coalesce method to adjust the partition size of RDD based on our needs.
Filter rows which meet particular criteria
Map with case class
Use case class if you want to map on multiple columns with a complexdata structure.
Use selectExpr to access inner attributes
Provide easily access the nested data structures like
json and filter themusing any existing udfs, or use your udf to get more flexibility here.
How to access RDD methods from pyspark side
RDD operation via pyspark API isn’t straight forward, to get thatwe need to invoke the
.rdd to convert the DataFrame to support these features.
For example, here we are converting a sparse vector to dense and summing it in column-wise.
Pyspark Map on multiple columns
Filtering a DataFrame column of type Seq[String]
Filter a column with custom regex and udf
Spark Sql Dataframe Cheat Sheet
Sum a column elements
Remove Unicode characters from tokens
Sometimes we only need to work with the ascii text, so it’s better to clean outother chars.
Connecting to jdbc with partition by integer column
When using the spark to read data from the SQL database and then do theother pipeline processing on it, it’s recommended to partition the dataaccording to the natural segments in the data, or at least on an integercolumn, so that spark can fire multiple sql queries to read data from SQLserver and operate on it separately, the results are going to the sparkpartition.
Bellow commands are in pyspark, but the APIs are the same for the scala version also.
Parse nested json data
This will be very helpful when working with
pyspark and want to pass verynested json data between JVM and Python processes. Lately spark community relay onapache arrow project to avoid multiple serialization/deserialization costs whensending data from java memory to python memory or vice versa.
So to process the inner objects you can make use of this
getItem methodto filter out required parts of the object and pass it over to python memory viaarrow. In the future arrow might support arbitrarily nested data, but right now it won’tsupport complex nested formats. The general recommended option is to go without nesting.
'string ⇒ array<string>' conversion
.as[String] avoid implicit conversion assumed.
A crazy string collection and groupby
This is a stream of operation on a column of type
Array[String] and collectthe tokens and count the n-gram distribution over all the tokens.
How to access AWS s3 on spark-shell or pyspark
Most of the time we might require a cloud storage provider like s3 / gs etc, toread and write the data for processing, very few keeps in-house hdfs to handle the datathemself, but for majority, I think cloud storage easy to start with and don’t needto bother about the size limitations.
Supply the aws credentials via environment variable
Supply the credentials via default aws ~/.aws/config file
Recent versions of
awscli expect its configurations are kept under
~/.aws/credentials file,but old versions looks at
~/.aws/config path, spark 2.4.x version now looks at the
~/.aws/config locationsince spark 2.4.x comes with default hadoop jars of version 2.7.x.
Set spark scratch space or tmp directory correctly
Pyspark Cheat Sheet
This might require when working with a huge dataset and your machine can’t hold themall in memory for given pipeline steps, those cases the data will be spilled overto disk, and saved in tmp directory.
Set bellow properties to ensure, you have enough space in tmp location.
Pyspark doesn’t support all the data types.
When using the
arrow to transport data between jvm to python memory, the arrow may throwbellow error if the types aren’t compatible to existing converters. The fixes may becomein the future on the arrow’s project. I’m keeping this here to know that how the pyspark getsdata from jvm and what are those things can go wrong in that process.
Work with spark standalone cluster manager
Start the spark clustering in standalone mode
Once you have downloaded the same version of the spark binary across the machinesyou can start the spark master and slave processes to form the standalone sparkcluster. Or you could run both these services on the same machine also.
Worker can have multiple executors.
Worker is like a node manager in yarn.
We can set worker max core and memory usage settings.
When defining the spark application via spark-shell or so, define the executor memory and cores.
When submitting the job to get 10 executor with 1 cpu and 2gb ram each,
Pyspark Vs Spark Sql
|This page will be updated as and when I see some reusable snippet of code for spark operations|