snappy | Helps you browse through and interpret your genotype data

 by   zhaofengli JavaScript Version: Current License: BSD-2-Clause

kandi X-RAY | snappy Summary

kandi X-RAY | snappy Summary

snappy is a JavaScript library. snappy has no bugs, it has a Permissive License and it has low support. However snappy has 1 vulnerabilities. You can download it from GitHub.

Snappy helps you browse through and interpret your genotype data, right on your browser. It accepts raw genotype files from 23andMe, AncestryDNA and FamilyTreeDNA. All processing is done on the browser, without sending sensitive data over the wire.
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            kandi-support Support

              snappy has a low active ecosystem.
              It has 35 star(s) with 7 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 1 open issues and 0 have been closed. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of snappy is current.

            kandi-Quality Quality

              snappy has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              snappy is licensed under the BSD-2-Clause License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              snappy releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.
              It has 293 lines of code, 12 functions and 48 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed snappy and discovered the below as its top functions. This is intended to give you an instant insight into snappy implemented functionality, and help decide if they suit your requirements.
            • Process the package . data .
            • Generate CSS loader
            Get all kandi verified functions for this library.

            snappy Key Features

            No Key Features are available at this moment for snappy.

            snappy Examples and Code Snippets

            No Code Snippets are available at this moment for snappy.

            Community Discussions

            QUESTION

            Dynamic stage path in snowflake
            Asked 2022-Mar-14 at 10:31

            I have a stage path as below

            ...

            ANSWER

            Answered 2022-Mar-14 at 10:31

            Here is one approach. Your stage shouldn't include the date as part of the stage name because if it did, you would need a new stage every day. Better to define the stage as company_stage/pbook/.

            To make it dynamic, I suggest using the pattern option together with the COPY INTO command. You could create a variable with the regex pattern expression using current_date(), something like this:

            Source https://stackoverflow.com/questions/71453827

            QUESTION

            Jetpack Compose LazyRow scroll with snap only to start of next or previous element
            Asked 2022-Mar-10 at 18:17

            Is there a way to horizontally scroll only to start or specified position of previous or next element with Jetpack Compose?

            Snappy scrolling in RecyclerView

            ...

            ANSWER

            Answered 2021-Aug-22 at 19:08

            You can check the scrolling direction like so

            Source https://stackoverflow.com/questions/68882038

            QUESTION

            Spring Boot Logging to a File
            Asked 2022-Feb-16 at 14:49

            In my application config i have defined the following properties:

            ...

            ANSWER

            Answered 2022-Feb-16 at 13:12

            Acording to this answer: https://stackoverflow.com/a/51236918/16651073 tomcat falls back to default logging if it can resolve the location

            Can you try to save the properties without the spaces.

            Like this: logging.file.name=application.logs

            Source https://stackoverflow.com/questions/71142413

            QUESTION

            The Kafka topic is here, a Java consumer program finds it, but lists none of its content, while a kafka-console-consumer is able to
            Asked 2022-Feb-16 at 13:23

            It's my first Kafka program.

            From a kafka_2.13-3.1.0 instance, I created a Kafka topic poids_garmin_brut and filled it with this csv:

            ...

            ANSWER

            Answered 2022-Feb-15 at 14:36

            Following should work.

            Source https://stackoverflow.com/questions/71122596

            QUESTION

            Error when running Pytest with DeltaTables
            Asked 2022-Feb-14 at 10:18

            I am working in the VDI of a company and they use their own artifactory for security reasons. Currently I am writing unit tests to perform tests for a function that deletes entries from a delta table. When I started, I received an error of unresolved dependencies, because my spark session was configured in a way that it would load jars from maven. I was able to solve this issue by loading these jars locally from /opt/spark/jars. Now my code looks like this:

            ...

            ANSWER

            Answered 2022-Feb-14 at 10:18

            It looks like that you're using incompatible version of the Delta lake library. 0.7.0 was for Spark 3.0, but you're using another version - either lower, or higher. Consult Delta releases page to find mapping between Delta version & required Spark versions.

            If you're using Spark 3.1 or 3.2, consider using delta-spark Python package that will install all necessary dependencies, so you just import DeltaTable class.

            Update: Yes, this happens because of the conflicting versions - you need to remove delta-spark and pyspark Python package, and install pyspark==3.0.2 explicitly.

            P.S. Also, look onto pytest-spark package that can simplify specification of configuration for all tests. You can find examples of it + Delta here.

            Source https://stackoverflow.com/questions/71084507

            QUESTION

            How can I have nice file names & efficient storage usage in my Foundry Magritte dataset export?
            Asked 2022-Feb-10 at 05:12

            I'm working on exporting data from Foundry datasets in parquet format using various Magritte export tasks to an ABFS system (but the same issue occurs with SFTP, S3, HDFS, and other file based exports).

            The datasets I'm exporting are relatively small, under 512 MB in size, which means they don't really need to be split across multiple parquet files, and putting all the data in one file is enough. I've done this by ending the previous transform with a .coalesce(1) to get all of the data in a single file.

            The issues are:

            • By default the file name is part-0000-.snappy.parquet, with a different rid on every build. This means that, whenever a new file is uploaded, it appears in the same folder as an additional file, the only way to tell which is the newest version is by last modified date.
            • Every version of the data is stored in my external system, this takes up unnecessary storage unless I frequently go in and delete old files.

            All of this is unnecessary complexity being added to my downstream system, I just want to be able to pull the latest version of data in a single step.

            ...

            ANSWER

            Answered 2022-Jan-13 at 15:27

            This is possible by renaming the single parquet file in the dataset so that it always has the same file name, that way the export task will overwrite the previous file in the external system.

            This can be done using raw file system access. The write_single_named_parquet_file function below validates its inputs, creates a file with a given name in the output dataset, then copies the file in the input dataset to it. The result is a schemaless output dataset that contains a single named parquet file.

            Notes

            • The build will fail if the input contains more than one parquet file, as pointed out in the question, calling .coalesce(1) (or .repartition(1)) is necessary in the upstream transform
            • If you require transaction history in your external store, or your dataset is much larger than 512 MB this method is not appropriate, as only the latest version is kept, and you likely want multiple parquet files for use in your downstream system. The createTransactionFolders (put each new export in a different folder) and flagFile (create a flag file once all files have been written) options can be useful in this case.
            • The transform does not require any spark executors, so it is possible to use @configure() to give it a driver only profile. Giving the driver additional memory should fix out of memory errors when working with larger datasets.
            • shutil.copyfileobj is used because the 'files' that are opened are actually just file objects.

            Full code snippet

            example_transform.py

            Source https://stackoverflow.com/questions/70652943

            QUESTION

            Upserts on Delta simply duplicates data?
            Asked 2022-Feb-07 at 07:22

            I'm fairly new with Delta and lakehouse on databricks. I have some questions, based on the following actions:

            • I import some parquet files
            • Convert them to delta (creating 1 snappy.parquet file)
            • Delete one random row (creating 1 new snappy.parquet file).
            • I check content of both snappy files (version 0 of delta table, and version1), and they both contain all of the data, each one with it's specific differences.

            Does this mean delta simply duplicates data for every new version?

            How is this scalable? or am I missing something?

            ...

            ANSWER

            Answered 2022-Feb-07 at 07:22

            Yes, that's how Delta lake works - when you're doing modification of the data, it won't write only delta, but takes the original file that is affected by change, make changes, and write it back. But take into account that not all data is duplicated - only that were in the file where affected rows are. For example, you have 3 data files, and you're making changes to some rows that are in the 2nd file. In this case, Delta will create a new file with number 4 that contains necessary changes + the rest of data from file 2, so you will have following versions:

            • Version 0: files 1, 2 & 3
            • Version 1: files, 1, 3 & 4

            Source https://stackoverflow.com/questions/71010769

            QUESTION

            OWL API NoSuchMethodError in saveOntology() call
            Asked 2022-Jan-31 at 10:43

            I am trying to call an OWL API java program through terminal and it crashes, while the exact same code is running ok when I run it in IntelliJ.

            The exception that rises in my main code is this:

            ...

            ANSWER

            Answered 2022-Jan-31 at 10:43

            As can be seen in the comments of the post, my problem is fixed, so I thought I'd collect a closing answer here to not leave the post pending.

            The actual solution: As explained here nicely by @UninformedUser, the issue was that I had conflicting maven package versions in my dependencies. Bringing everything in sync with each other solved the issue.

            Incidental solution: As I wrote in the comments above, specifically defining 3.3.0 for the maven-assembly-plugin happened to solve the issue. But this was only chance, as explained here by @Ignazio, just because the order of "assembling" things changed, overwriting the conflicting package.

            Huge thanks to both for the help.

            Source https://stackoverflow.com/questions/70854565

            QUESTION

            pyarrow reading parquet from S3 performance confusions
            Asked 2022-Jan-26 at 19:16

            I have a Parquet file in AWS S3. I would like to read it into a Pandas DataFrame. There are two ways for me to accomplish this.

            ...

            ANSWER

            Answered 2022-Jan-26 at 19:16

            You are correct. Option 2 is just option 1 under the hood.

            What is the fastest way for me to read a Parquet file into Pandas?

            Both option 1 and option 2 are probably good enough. However, if you are trying to shave off every bit you may need to go one layer deeper, depending on your pyarrow version. It turns out that Option 1 is actually also just a proxy, in this case to the datasets API:

            Source https://stackoverflow.com/questions/70857825

            QUESTION

            Dask ParserError: Error tokenizing data when reading CSV
            Asked 2022-Jan-19 at 17:11

            I am getting the same error as this question, but the recommended solution of setting blocksize=None isn't solving the issue for me. I'm trying to convert the NYC taxi data from CSV to Parquet and this is the code I'm running:

            ...

            ANSWER

            Answered 2022-Jan-19 at 17:08

            The raw file s3://nyc-tlc/trip data/yellow_tripdata_2010-02.csv contains an error (one too many commas). This is the offending line (middle) and its neighbours:

            Source https://stackoverflow.com/questions/70763876

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

            Vulnerabilities

            Memcpy parameter overlap in Google Snappy library 1.1.4, as used in Google TensorFlow before 1.7.1, could result in a crash or read from other parts of process memory.

            Install snappy

            You need Yarn to install dependencies. Simply run yarn then yarn run dev to fire up the dev server. To build for production, run yarn run build. npm will work, but it's not recommended. Cats love yarn, and you will love it, too.
            Snappy depends on data from SNPedia to work. A copy is included in the repo but may be out-of-date. To generate an updated copy, you need Python 3:. The scripts will download and parse SNPedia, finally generating JSON files Snappy can consume. Copy those files to data.

            Support

            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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            CLONE
          • HTTPS

            https://github.com/zhaofengli/snappy.git

          • CLI

            gh repo clone zhaofengli/snappy

          • sshUrl

            git@github.com:zhaofengli/snappy.git

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