sql-dataset | Run SQL queries and send the results to Geckoboard Datasets | SQL Database library

by geckoboard Go Version: v0.2.4 License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | sql-dataset Summary

sql-dataset is a Go library typically used in Database, SQL Database, PostgresSQL, MariaDB applications. sql-dataset has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Quickly and easily send data from Microsoft SQL Server, MySQL, Postgres and SQLite databases to Geckoboard Datasets. SQL-Dataset is a command line app that takes the hassle out of integrating your database with Geckoboard. Rather than having to work with client libraries and write a bunch of code to connect to and query your database, with SQL-Dataset all you need to do is fill out a simple config file. SQL-Dataset is available for macOS, Linux, and Windows.

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Support

sql-dataset has a low active ecosystem.

It has 25 star(s) with 8 fork(s). There are 17 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 8 have been closed. On average issues are closed in 56 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of sql-dataset is v0.2.4

Quality

sql-dataset has 0 bugs and 0 code smells.

Security

sql-dataset has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

sql-dataset code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

sql-dataset is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

sql-dataset releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 5170 lines of code, 63 functions and 18 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed sql-dataset and discovered the below as its top functions. This is intended to give you an instant insight into sql-dataset implemented functionality, and help decide if they suit your requirements.

Generate the dataset
SendAllData will send all rows to the Dataset .
Process all datasets
Delete a dataset switch
LoadConfig loads config from a file
handleResponse returns an error if the response is not valid .
newDBConnection creates a new sql . DB connection .
NewConnStringBuilder returns a new ConnStringBuilder .
convertEnvToValue converts a string to a value
Build params

Get all kandi verified functions for this library.

sql-dataset Key Features

No Key Features are available at this moment for sql-dataset.

sql-dataset Examples and Code Snippets

No Code Snippets are available at this moment for sql-dataset.

Community Discussions

Trending Discussions on sql-dataset

Store execution plan of Spark´s dataframe

Spark Structured Streaming and Spark-Ml Regression

QUESTION

Store execution plan of Spark´s dataframe

Asked 2019-Jan-07 at 17:16

I am currently trying to store the execution plan of a Spark´s dataframe into HDFS (through dataframe.explain(true) command)

The issue I am finding is that when I am using the explain(true) command, I am able to see the output by the command line and by the logs, however if I create a file (let´s say a .txt) with the content of the dataframe´s explain the file will appear empty.

I believe the issue relates to the configuration of Spark, but I am unable to find any information about this in internet

(for those who want to see more about the plan execution of the dataframes using the explain function please refer to https://jaceklaskowski.gitbooks.io/mastering-apache-spark/spark-sql-dataset-operators.html#explain)

...

ANSWER

Answered 2017-Aug-31 at 14:45

if I create a file (let´s say a .txt) with the content of the dataframe´s explain

How exactly did you try to achieve this?

explain writes its result to console, using println, and returns Unit, as can be seen in Dataset.scala:

Source https://stackoverflow.com/questions/45981710

QUESTION

Spark Structured Streaming and Spark-Ml Regression

Asked 2018-Jan-14 at 11:43

Is it possible to apply Spark-Ml regression to streaming sources? I see there is StreamingLogisticRegressionWithSGD but It's for older RDD API and I couldn't use It with structured streaming sources.

How I'm supposed to apply regressions on structured streaming sources?
(A little OT) If I cannot use streaming API for regression how can I commit offsets or so to source in a batch processing way? (Kafka sink)

...

ANSWER

Answered 2018-Jan-14 at 11:43

Today (Spark 2.2 / 2.3) there is no support for machine learning in Structured Streaming and there is no ongoing work in this direction. Please follow SPARK-16424 to track future progress.

You can however:

Train iterative, non-distributed models using forEach sink and some form of external state storage. At a high level regression model could be implemented like this:
- Fetch latest model when calling ForeachWriter.open and initialize loss accumulator (not in a Spark sense, just local variable) for the partition.
- Compute loss for each record in ForeachWriter.process and update accumulator.
- Push loses to external store when calling ForeachWriter.close.
- This would leave external storage in charge with computing gradient and updating model with implementation dependent on the store.
Try to hack SQL queries (see https://github.com/holdenk/spark-structured-streaming-ml by Holden Karau)

Source https://stackoverflow.com/questions/48249017

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

Vulnerabilities

No vulnerabilities reported

Install sql-dataset

On macOS and Linux you'll need to open a terminal and run chmod u+x path/to/file (replacing path/to/file with the actual path to your downloaded app) in order to make the app executable.
macOS
Linux x86 / x64
Windows x86 / x64

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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