spark-ml-serving-common


License

License

GroupId

GroupId

io.hydrosphere
ArtifactId

ArtifactId

spark-ml-serving-common_2.11
Last Version

Last Version

0.2.2
Release Date

Release Date

Type

Type

jar
Description

Description

spark-ml-serving-common
spark-ml-serving-common
Project URL

Project URL

https://github.com/Hydrospheredata/spark-ml-serving
Project Organization

Project Organization

io.hydrosphere
Source Code Management

Source Code Management

https://github.com/Hydrospheredata/mist.git

Download spark-ml-serving-common_2.11

How to add to project

<!-- https://jarcasting.com/artifacts/io.hydrosphere/spark-ml-serving-common_2.11/ -->
<dependency>
    <groupId>io.hydrosphere</groupId>
    <artifactId>spark-ml-serving-common_2.11</artifactId>
    <version>0.2.2</version>
</dependency>
// https://jarcasting.com/artifacts/io.hydrosphere/spark-ml-serving-common_2.11/
implementation 'io.hydrosphere:spark-ml-serving-common_2.11:0.2.2'
// https://jarcasting.com/artifacts/io.hydrosphere/spark-ml-serving-common_2.11/
implementation ("io.hydrosphere:spark-ml-serving-common_2.11:0.2.2")
'io.hydrosphere:spark-ml-serving-common_2.11:jar:0.2.2'
<dependency org="io.hydrosphere" name="spark-ml-serving-common_2.11" rev="0.2.2">
  <artifact name="spark-ml-serving-common_2.11" type="jar" />
</dependency>
@Grapes(
@Grab(group='io.hydrosphere', module='spark-ml-serving-common_2.11', version='0.2.2')
)
libraryDependencies += "io.hydrosphere" % "spark-ml-serving-common_2.11" % "0.2.2"
[io.hydrosphere/spark-ml-serving-common_2.11 "0.2.2"]

Dependencies

compile (7)

Group / Artifact Type Version
org.scala-lang : scala-library jar 2.11.8
org.json4s : json4s-native_2.11 jar 3.2.10
com.twitter : parquet-hadoop-bundle jar 1.6.0
org.apache.parquet : parquet-common jar 1.7.0
org.apache.parquet : parquet-column jar 1.7.0
org.apache.parquet : parquet-hadoop jar 1.7.0
org.apache.parquet : parquet-avro jar 1.7.0

provided (1)

Group / Artifact Type Version
org.apache.spark : spark-mllib_2.11 jar 2.0.0

Project Modules

There are no modules declared in this project.

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Hydrosphere Mist

Join the chat at https://gitter.im/Hydrospheredata/mist

Hydrosphere Mist is a serverless proxy for Spark cluster. Mist provides a new functional programming framework and deployment model for Spark applications.

Please see our quick start guide and documentation

Features:

  • Spark Function as a Service. Deploy Spark functions rather than notebooks or scripts.
  • Spark Cluster and Session management. Fully managed Spark sessions backed by on-demand EMR, Hortonworks, Cloudera, DC/OS and vanilla Spark clusters.
  • Typesafe programming framework that clearly defines inputs and outputs of every Spark job.
  • REST HTTP & Messaging (MQTT, Kafka) API for Scala & Python Spark jobs.
  • Multi-cluster mode: Seamless Spark cluster on-demand provisioning, autoscaling and termination(pending) Cluster of Spark Clusters

It creates a unified API layer for building enterprise solutions and microservices on top of a Spark functions.

Mist use cases

High Level Architecture

High Level Architecture

Contact

Please report bugs/problems to: https://github.com/Hydrospheredata/mist/issues.

http://hydrosphere.io/

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io.hydrosphere

hydrosphere.io

Versions

Version
0.2.2
0.2.1
0.2.0