spark | 『빅데이터 분석을 위한 스파크 2 프로그래밍』 예제 코드

 by   wikibook Java Version: Current License: No License

kandi X-RAY | spark Summary

kandi X-RAY | spark Summary

spark is a Java library. spark has no bugs, it has build file available and it has low support. However spark has 4 vulnerabilities. You can download it from GitHub.

『빅데이터 분석을 위한 스파크 2 프로그래밍』 예제 코드

            kandi-support Support

              spark has a low active ecosystem.
              It has 25 star(s) with 14 fork(s). There are 6 watchers for this library.
              It had no major release in the last 6 months.
              There are 2 open issues and 0 have been closed. On average issues are closed in 698 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of spark is current.

            kandi-Quality Quality

              spark has 0 bugs and 0 code smells.

            kandi-Security Security

              spark has 4 vulnerability issues reported (1 critical, 1 high, 2 medium, 0 low).
              spark code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              spark does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              spark releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed spark and discovered the below as its top functions. This is intended to give you an instant insight into spark implemented functionality, and help decide if they suit your requirements.
            • An example of Spark DataFrame Sample
            • Creates the data frame
            • The main runner
            • Starts Spark streaming sample
            • Command line parser
            • Creates a sample indexer sample
            • The main entry point
            • Zip a parallel partition
            • Makes a map of partitions
            • Shortcut for testing
            • Starts a SparkSampleSampleSample
            • Run basic data
            • Main method for testing
            • Entry point to the Spark sample sample
            • Demonstrates how to show a sample
            • Example of running Spark Session
            • Sample sample
            • 1 2
            • Run date functions
            • The main sample sample
            • Run other functions
            • Main method for testing
            Get all kandi verified functions for this library.

            spark Key Features

            No Key Features are available at this moment for spark.

            spark Examples and Code Snippets

            No Code Snippets are available at this moment for spark.

            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


            CVE-2020-9480 CRITICAL
            In Apache Spark 2.4.5 and earlier, a standalone resource manager's master may be configured to require authentication (spark.authenticate) via a shared secret. When enabled, however, a specially-crafted RPC to the master can succeed in starting an application's resources on the Spark cluster, even without the shared key. This can be leveraged to execute shell commands on the host machine. This does not affect Spark clusters using other resource managers (YARN, Mesos, etc).
            In all versions of Apache Spark, its standalone resource manager accepts code to execute on a 'master' host, that then runs that code on 'worker' hosts. The master itself does not, by design, execute user code. A specially-crafted request to the master can, however, cause the master to execute code too. Note that this does not affect standalone clusters with authentication enabled. While the master host typically has less outbound access to other resources than a worker, the execution of code on the master is nevertheless unexpected.

            Install spark

            You can download it from GitHub.
            You can use spark like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the spark component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer For Gradle installation, please refer .


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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