spark | Firely and Incendi 's open source FHIR server

 by   FirelyTeam C# Version: v1.5.16-stu3 License: Non-SPDX

kandi X-RAY | spark Summary

kandi X-RAY | spark Summary

spark is a C# library typically used in Big Data, Spark applications. spark has no bugs and it has low support. However spark has 2 vulnerabilities and it has a Non-SPDX License. You can download it from GitHub.

Spark is an open-source FHIR server developed in C#, initially built by Firely. Further development and maintenance is now done by Incendi. Spark implements a major part of the FHIR specification and has been used and tested during several HL7 WGM Connectathons.
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              spark has a low active ecosystem.
              It has 224 star(s) with 161 fork(s). There are 45 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 48 open issues and 198 have been closed. On average issues are closed in 107 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of spark is v1.5.16-stu3

            kandi-Quality Quality

              spark has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              spark has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              spark releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.
              spark saves you 4613 person hours of effort in developing the same functionality from scratch.
              It has 14562 lines of code, 0 functions and 505 files.
              It has low 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

            No Code Snippets are available at this moment for spark.

            Community Discussions

            QUESTION

            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:

            ...

            ANSWER

            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

            Source https://stackoverflow.com/questions/70317481

            QUESTION

            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):

            ...

            ANSWER

            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:

            Source https://stackoverflow.com/questions/69316256

            QUESTION

            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:

            ...

            ANSWER

            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.

            Source https://stackoverflow.com/questions/68625748

            QUESTION

            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:

            ...

            ANSWER

            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:

            Source https://stackoverflow.com/questions/68891312

            QUESTION

            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) :

            ...

            ANSWER

            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.

            Source https://stackoverflow.com/questions/69480470

            QUESTION

            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.

            ...

            ANSWER

            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.:

            Source https://stackoverflow.com/questions/70901724

            QUESTION

            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 (https://docs.docker.com/compose/compose-file/compose-file-v3/#env_file) 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

            ...

            ANSWER

            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: https://docs.docker.com/compose/extends/#understanding-multiple-compose-files

            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: https://github.com/docker/compose/issues/3874#issuecomment-470311052

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

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

            Or in my case:

            Source https://stackoverflow.com/questions/70414774

            QUESTION

            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

            ...

            ANSWER

            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

            Source https://stackoverflow.com/questions/70339062

            QUESTION

            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?

            ...

            ANSWER

            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.

            Theory

            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.

            Design

            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:

            Source https://stackoverflow.com/questions/70220574

            QUESTION

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

            ANSWER

            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:

            Source https://stackoverflow.com/questions/70105647

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

            Vulnerabilities

            No vulnerabilities reported

            Install spark

            There are two ways to get started with Spark. Either by using the NuGet packages and following the Quickstart Tutorial, or by using the Docker Images.

            Support

            If you want to contribute, see our [guidelines](https://github.com/furore-fhir/spark/wiki/Contributing).
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