Apache Spark

Apache Spark is an open source distributed general-purpose cluster-computing framework. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.
Here are 5,941 public repositories matching this topic...
-
Updated
May 13, 2021 - Python
-
Updated
Sep 10, 2021 - Python
-
Updated
Jul 4, 2021 - Go
-
Updated
Sep 13, 2021 - Python
Describe the bug
Serverless: Deprecation warning: Variables resolver reports following resolution errors:
- Cannot resolve variable at "provider.environment.CUBEJS_APP": Value not found at "self" source,
- Cannot resolve variable at "functions.cubejsProcess.events.0.event.resource": Value not found at "self" source
From a next major this will be
-
Updated
Aug 14, 2021 - Java
-
Updated
Aug 3, 2021
-
Updated
Sep 14, 2021 - Java
-
Updated
Oct 31, 2019
-
Updated
Aug 18, 2021 - Java
-
Updated
Sep 14, 2021 - Jupyter Notebook
-
Updated
Dec 23, 2020 - Python
-
Updated
Sep 15, 2021 - Java
-
Updated
Apr 24, 2020 - Jsonnet
-
Updated
Jul 28, 2021 - Scala
-
Updated
Jul 14, 2021 - Python
Could we clarify that delta-log files are JSON line-delimited files in https://github.com/delta-io/delta/blob/master/PROTOCOL.md#delta-log-entries ?
In the PROTOCOL.md file it is not clear what is the format of JSON. Every delta-log entry file is "new-line delimited json file", but this is not specified in this file. Protocol do not explicitly specify that every action is stored as a single-lin
-
Updated
May 26, 2019 - Scala
-
Updated
Mar 31, 2021 - JavaScript
-
Updated
May 12, 2021 - Jupyter Notebook
Used Spark version
Spark Version: 2.4.4
Used Spark Job Server version
SJS version: v0.11.1
Deployed mode
client on Spark Standalone
Actual (wrong) behavior
I can't get config, when post a job with 'sync=true'. I got it:
http://localhost:8090/jobs/ff99479b-e59c-4215-b17d-4058f8d97d25/config
{"status":"ERROR","result":"No such job ID ff99479b-e59c-4215-b17d-4058f8d97d25"
I have a simple regression task (using a LightGBMRegressor) where I want to penalize negative predictions more than positive ones. Is there a way to achieve this with the default regression LightGBM objectives (see https://lightgbm.readthedocs.io/en/latest/Parameters.html)? If not, is it somehow possible to define (many example for default LightGBM model) and pass a custom regression objective?
Created by Matei Zaharia
Released May 26, 2014
- Repository
- apache/spark
- Website
- spark.apache.org
- Wikipedia
- Wikipedia
At this moment relu_layer op doesn't allow threshold configuration, and legacy RELU op allows that.
We should add configuration option to relu_layer.