spark | Apache Spark - A unified analytics engine

 by   apache Scala Version: v3.2.4 License: Apache-2.0

kandi X-RAY | spark Summary

kandi X-RAY | spark Summary

spark is a Scala library typically used in Big Data, Kafka, Spark, Hadoop applications. spark has no bugs, it has a Permissive License and it has medium support. However spark has 7 vulnerabilities. You can download it from GitHub.

Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs. Please refer to the build documentation at "Specifying the Hadoop Version and Enabling YARN" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.

            kandi-support Support

              spark has a medium active ecosystem.
              It has 35985 star(s) with 27039 fork(s). There are 2041 watchers for this library.
              It had no major release in the last 6 months.
              spark has no issues reported. There are 276 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of spark is v3.2.4

            kandi-Quality Quality

              spark has 0 bugs and 0 code smells.

            kandi-Security Security

              spark has 7 vulnerability issues reported (1 critical, 5 high, 1 medium, 0 low).
              spark code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              spark is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              spark releases are not available. You will need to build from source code and install.
              Installation instructions are not available. Examples and code snippets are available.
              It has 958877 lines of code, 58210 functions and 5909 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

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            spark Key Features

            No Key Features are available at this moment for spark.

            spark Examples and Code Snippets

            make an 'AND' condition with all elements in the list
            Pythondot img1Lines of Code : 30dot img1License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            filters = ['category', 'name']
            for col in x.columns:
                if all(x in col for x in filters):
            x = pd.DataFrame([['flow', 'x', 'cate
            make an 'AND' condition with all elements in the list
            Pythondot img2Lines of Code : 9dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            reg_str = '&'.join(filters)
              category_name_courses category_name_area
            0                 Spark              cloud
            1               PySpark              cloud
            2                Python      
            For-Loop inside of lambda in pyspark
            Pythondot img3Lines of Code : 18dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            sc = spark.sparkContext
            rdd = sc.parallelize(["abcdefg", "hijklmno"])  
            # Out: ['abcdefg', 'hijklmno']
   x: '-'.join([x[i:i+2] for i in range(0, len(x), 2)])).collect()
            # Out:['ab-cd-ef-g', 'hi-jk-lm-no']
            How to use pyspark.rdd to combine the data format like ("word", (1, 2))?
            Pythondot img4Lines of Code : 4dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            rdd.reduceByKey(lambda a, b: (a[0] + b[0], a[1] + b[1])).collect()
            # [('word1', (3, 5)), ('word2', (8, 10))]
            Identify field not in dataframe using Spark
            Pythondot img5Lines of Code : 4dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            init_cols = df.columns
            new_cols = new_df.columns
            result = ','.join([c for c in new_cols if c not in init_cols])
            Replacing column values in nested structure spark dataframe
            Pythondot img6Lines of Code : 15dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from itertools import chain
            import pyspark.sql.functions as F
            keys_map = F.create_map(*[F.lit(x)for x in chain(*keys.items())])
            df = df.withColumn(
            Filter pyspark DataFrame by string match
            Pythondot img7Lines of Code : 20dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df['match'] = [set(c.split()).intersection(k.split(',')) > set()
                           for c,k in zip(df['comments'], df['keywords'])]
               name               comments                keywords  match
            0  paul      account 
            Filter pyspark DataFrame by string match
            Pythondot img8Lines of Code : 8dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            keys = ('|').join([f'({x})' for x in df['keywords'].iloc[0].split(',')])
            0    True
            1    True
            2    True
            Name: comments, dtype: bool
            How to join two RDD's in PySpark?
            Pythondot img9Lines of Code : 15dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            rdd1 = sc.parallelize([('python', 36), ('c', 6), ('c#', 8)])
            rdd2 = sc.parallelize([('python', 10), ('c', 1), ('c#', 1)])
            rdd1.join(rdd2).map(lambda x: (x[0], *x[1])).toDF().show()
            |    _1| _2| _3|
            Pyspark add row based on a condition
            Pythondot img10Lines of Code : 42dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df = spark.createDataFrame([
                (1, 'open', '01.01.22 10:05:04'),
                (1, 'In process', '01.01.22 10:07:02'),
            ], ['a', 'b', 'c'])
            |  a|         b|                c|

            Community Discussions


            spark-shell throws java.lang.reflect.InvocationTargetException on running
            Asked 2022-Apr-01 at 19:53

            When I execute run-example SparkPi, for example, it works perfectly, but when I run spark-shell, it throws these exceptions:



            Answered 2022-Jan-07 at 15:11

            i face the same problem, i think Spark 3.2 is the problem itself

            switched to Spark 3.1.2, it works fine



            Why joining structure-identic dataframes gives different results?
            Asked 2022-Mar-21 at 13:05

            Update: the root issue was a bug which was fixed in Spark 3.2.0.

            Input df structures are identic in both runs, but outputs are different. Only the second run returns desired result (df6). I know I can use aliases for dataframes which would return desired result.

            The question. What is the underlying Spark mechanics in creating df3? Spark reads df1.c1 == df2.c2 in the join's on clause, but it's evident that it does not pay attention to the dfs provided. What's under the hood there? How to anticipate such behaviour?

            First run (incorrect df3 result):



            Answered 2021-Sep-24 at 16:19

            Spark for some reason doesn't distinguish your c1 and c2 columns correctly. This is the fix for df3 to have your expected result:



            AttributeError: Can't get attribute 'new_block' on
            Asked 2022-Feb-25 at 13:18

            I was using pyspark on AWS EMR (4 r5.xlarge as 4 workers, each has one executor and 4 cores), and I got AttributeError: Can't get attribute 'new_block' on . Below is a snippet of the code that threw this error:



            Answered 2021-Aug-26 at 14:53

            I had the same error using pandas 1.3.2 in the server while 1.2 in my client. Downgrading pandas to 1.2 solved the problem.



            Problems when writing parquet with timestamps prior to 1900 in AWS Glue 3.0
            Asked 2022-Feb-10 at 13:45

            When switching from Glue 2.0 to 3.0, which means also switching from Spark 2.4 to 3.1.1, my jobs start to fail when processing timestamps prior to 1900 with this error:



            Answered 2022-Feb-10 at 13:45

            I made it work by setting --conf to spark.sql.legacy.parquet.int96RebaseModeInRead=CORRECTED --conf spark.sql.legacy.parquet.int96RebaseModeInWrite=CORRECTED --conf spark.sql.legacy.parquet.datetimeRebaseModeInRead=CORRECTED --conf spark.sql.legacy.parquet.datetimeRebaseModeInWrite=CORRECTED.

            This is a workaround though and Glue Dev team is working on a fix, although there is no ETA.

            Also this is still very buggy. You can not call .show() on a DynamicFrame for example, you need to call it on a DataFrame. Also all my jobs failed where I call data_frame.rdd.isEmpty(), don't ask me why.

            Update 24.11.2021: I reached out to the Glue Dev Team and they told me that this is the intended way of fixing it. There is a workaround that can be done inside of the script though:



            NoSuchMethodError on com.fasterxml.jackson.dataformat.xml.XmlMapper.coercionConfigDefaults()
            Asked 2022-Feb-09 at 12:31

            I'm parsing a XML string to convert it to a JsonNode in Scala using a XmlMapper from the Jackson library. I code on a Databricks notebook, so compilation is done on a cloud cluster. When compiling my code I got this error java.lang.NoSuchMethodError: com.fasterxml.jackson.dataformat.xml.XmlMapper.coercionConfigDefaults()Lcom/fasterxml/jackson/databind/cfg/MutableCoercionConfig; with a hundred lines of "at com.databricks. ..."

            I maybe forget to import something but for me this is ok (tell me if I'm wrong) :



            Answered 2021-Oct-07 at 12:08

            Welcome to dependency hell and breaking changes in libraries.

            This usually happens, when various lib bring in different version of same lib. In this case it is Jackson. java.lang.NoSuchMethodError: com.fasterxml.jackson.dataformat.xml.XmlMapper.coercionConfigDefaults()Lcom/fasterxml/jackson/databind/cfg/MutableCoercionConfig; means: One lib probably require Jackson version, which has this method, but on class path is version, which does not yet have this funcion or got removed bcs was deprecated or renamed.

            In case like this is good to print dependency tree and check version of Jackson required in libs. And if possible use newer versions of requid libs.

            Solution: use libs, which use compatible versions of Jackson lib. No other shortcut possible.



            Cannot find conda info. Please verify your conda installation on EMR
            Asked 2022-Feb-05 at 00:17

            I am trying to install conda on EMR and below is my bootstrap script, it looks like conda is getting installed but it is not getting added to environment variable. When I manually update the $PATH variable on EMR master node, it can identify conda. I want to use conda on Zeppelin.

            I also tried adding condig into configuration like below while launching my EMR instance however I still get the below mentioned error.



            Answered 2022-Feb-05 at 00:17

            I got the conda working by modifying the script as below, emr python versions were colliding with the conda version.:



            How to set Docker Compose `env_file` relative to `.yml` file when multiple `--file` option is used?
            Asked 2021-Dec-20 at 18:51

            I am trying to set my env_file configuration to be relative to each of the multiple docker-compose.yml file locations instead of relative to the first docker-compose.yml.

            The documentation ( suggests this should be possible:

            If you have specified a Compose file with docker-compose -f FILE, paths in env_file are relative to the directory that file is in.

            For example, when I issue



            Answered 2021-Dec-20 at 18:51

            It turns out that there's already an issue and discussion regarding this:

            The thread points out that this is the expected behavior and is documented here:

            When you use multiple configuration files, you must make sure all paths in the files are relative to the base Compose file (the first Compose file specified with -f). This is required because override files need not be valid Compose files. Override files can contain small fragments of configuration. Tracking which fragment of a service is relative to which path is difficult and confusing, so to keep paths easier to understand, all paths must be defined relative to the base file.

            There's a workaround within that discussion that works fairly well:

            The workaround is to use a ENV var that has a default:

            • ${PROXY:-.}/haproxy/conf:/usr/local/etc/haproxy

            Or in my case:



            Read spark data with column that clashes with partition name
            Asked 2021-Dec-17 at 16:15

            I have the following file paths that we read with partitions on s3



            Answered 2021-Dec-14 at 02:46

            Yes, we can read all the json files without partition columns. Directly use the parent folder path and it will load all partitions data into the data frame.

            After reading the data frame, you can use withColumn() function to rename the date field.

            Something like the following should work



            How do I parse xml documents in Palantir Foundry?
            Asked 2021-Dec-09 at 21:17

            I have a set of .xml documents that I want to parse.

            I previously have tried to parse them using methods that take the file contents and dump them into a single cell, however I've noticed this doesn't work in practice since I'm seeing slower and slower run times, often with one task taking tens of hours to run:

            The first transform of mine takes the .xml contents and puts it into a single cell, and a second transform takes this string and uses Python's xml library to parse the string into a document. This document I'm then able to extract properties from and return a DataFrame.

            I'm using a UDF to conduct the process of mapping the string contents to the fields I want.

            How can I make this faster / work better with large .xml files?



            Answered 2021-Dec-09 at 21:17

            For this problem, we're going to combine a couple of different techniques to make this code both testable and highly scalable.


            When parsing raw files, you have a couple of options you can consider:

            1. ❌ You can write your own parser to read bytes from files and convert them into data Spark can understand.
              • This is highly discouraged whenever possible due to the engineering time and unscalable architecture. It doesn't take advantage of distributed compute when you do this as you must bring the entire raw file to your parsing method before you can use it. This is not an effective use of your resources.
            2. ⚠ You can use your own parser library not made for Spark, such as the XML Python library mentioned in the question
              • While this is less difficult to accomplish than writing your own parser, it still does not take advantage of distributed computation in Spark. It is easier to get something running, but it will eventually hit a limit of performance because it does not take advantage of low-level Spark functionality only exposed when writing a Spark library.
            3. ✅ You can use a Spark-native raw file parser
              • This is the preferred option in all cases as it takes advantage of low-level Spark functionality and doesn't require you to write your own code. If a low-level Spark parser exists, you should use it.

            In our case, we can use the Databricks parser to great effect.

            In general, you should also avoid using the .udf method as it likely is being used instead of good functionality already available in the Spark API. UDFs are not as performant as native methods and should be used only when no other option is available.

            A good example of UDFs covering up hidden problems would be string manipulations of column contents; while you technically can use a UDF to do things like splitting and trimming strings, these things already exist in the Spark API and will be orders of magnitude faster than your own code.


            Our design is going to use the following:

            1. Low-level Spark-optimized file parsing done via the Databricks XML Parser
            2. Test-driven raw file parsing as explained here
            Wire the Parser

            First, we need to add the .jar to our spark_session available inside Transforms. Thanks to recent improvements, this argument, when configured, will allow you to use the .jar in both Preview/Test and at full build time. Previously, this would have required a full build but not so now.

            We need to go to our transforms-python/build.gradle file and add 2 blocks of config:

            1. Enable the pytest plugin
            2. Enable the condaJars argument and declare the .jar dependency

            My /transforms-python/build.gradle now looks like the following:



            docker build vue3 not compatible with element-ui on node:16-buster-slim
            Asked 2021-Dec-07 at 08:54
            • dockerfile:


            Answered 2021-Dec-07 at 08:54

            It seems that you have problems with peer dependencies, if you just set your npm to use legacy dependency logic to install your packages you will solve the problem.

            Just add to your Dockerfile this setting before running npm install:


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install spark

            You can download it from GitHub.


            You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
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