Cassandra + Spark + Elk

•

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Presentation made at scalaby #13 ~> http://scala.by

Cassandra+Spark+ELK
Dmitriy Kalyada @ 2015

What is Spark?
• Master: Driver program
• Workers: Executors
• High Availability
• Standby Masters with
ZooKeeper
• Single-Node Recovery with
Local File System

Under the hood
• Resilient Distributed Dataset
(RDD)
• Scala + Akka Framework
• Java, Scala, Python API
• Spark SQL, MLib, Spark
Streaming, GraphX

Our particular case
Devices
Cassandra
Spark
ELK

Data ﬂow
Fetcher Transformer Saver
Input Source(s)
x-RDD x-RDD
Output Source

Spark Cassandra Connector
• Represents Cassandra tables as Spark RDDs
• Write Spark RDDs to Cassandra tables
• Execute CQL queries in Spark applications
https://github.com/datastax/spark-cassandra-connector

CassandraRDD settings
• Connection params
• Fetching params
1. input.split.size: C* partitions in a Spark
Partition.
2. input.page.row.size: number of CQL rows
fetched per roundtrip.

Fetching essentials
…
…
…
…
-968391295277638458 … -893783532241185833
-968391295277638458, -893783532241185833
-7378580094811526501, -7340240117176401239
6426215139012569257, 6428979455828914106
-6094480671546553265, -6016282219056649738
-7259249675596554667, -7237838231745167324
-6734336817058726139, -6684208157211348972
-3891103372671105499, -3822513456325086923
4453206019575747361,4462441725813855391
7855385326468991461,7906589648045207141
-129433796439502583,-101280166181350027
-2233788032218452383,-2066644620711092198
3248662132571799756,3396129453515776704
7744134136205124749,7812918342246679728
-1408208314239486033,-1403736406052004344
• Support
Murmur3Partitioner
and
RandomPartitioner
• Retrieve token ranges
from Cassandra
• Prediction on base of 16
random token ranges

Data to RDD
…
Tokens Per RDD
[input.split.size]
Token Range #N
Slurp amount
[input.page.row.size]

Token range vs rows number

What to do?
• Change read strategy
• Split data on a smaller pieces
• Increase cluster strength
• Reorganize Cassandra schema

Elastic Search & Kibana
• Index initialization: TransportClient
• Create/Delete Index
• Setup Mappings
• Indexing: ScalaEsRDD
• Data presentation: Kibana

Kibana

Deployment
• Build package: Spark Job + Dependent Jars +
Conﬁgs
• Upload to the Spark Master Node
• Start job submit script

Thank you
dkaliada@exadel.com
Dmitriy Kalyada @ 2015

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Apache Spark 2.0 and subsequent releases of Spark 2.1 and 2.2 have laid the foundation for many new features and functionality. Its main three themes—easier, faster, and smarter—are pervasive in its unified and simplified high-level APIs for Structured data. In this introductory part lecture and part hands-on workshop, you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas: Agenda: • Overview of Spark Fundamentals & Architecture • What’s new in Spark 2.x • Unified APIs: SparkSessions, SQL, DataFrames, Datasets • Introduction to DataFrames, Datasets and Spark SQL • Introduction to Structured Streaming Concepts • Four Hands On Labs You will use Databricks Community Edition, which will give you unlimited free access to a ~6 GB Spark 2.x local mode cluster. And in the process, you will learn how to create a cluster, navigate in Databricks, explore a couple of datasets, perform transformations and ETL, save your data as tables and parquet files, read from these sources, and analyze datasets using DataFrames/Datasets API and Spark SQL. Level: Beginner to intermediate, not for advanced Spark users. Prerequisite: You will need a laptop with Chrome or Firefox browser installed with at least 8 GB. Introductory or basic knowledge Scala or Python is required, since the Notebooks will be in Scala; Python is optional. Bio: Jules S. Damji is an Apache Spark Community Evangelist with Databricks. He is a hands-on developer with over 15 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, LoudCloud/Opsware, VeriSign, Scalix, and ProQuest, building large-scale distributed systems. Before joining Databricks, he was a Developer Advocate at Hortonworks.

Jump Start on Apache® Spark™ 2.x with Databricks

Jump Start on Apache® Spark™ 2.x with Databricks

Jump Start on Apache® Spark™ 2.x with Databricks

In this introductory part lecture and part hands-on workshop, you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas: Agenda: • Overview of Spark Fundamentals & Architecture • What’s new in Spark 2.x • Unified APIs: SparkSessions, SQL, DataFrames, Datasets • Introduction to DataFrames, Datasets and Spark SQL • Introduction to Structured Streaming Concepts • Four Hands On Labs You will use Databricks Community Edition, which will give you unlimited free access to a ~6 GB Spark 2.x local mode cluster. And in the process, you will learn how to create a cluster, navigate in Databricks, explore a couple of datasets, perform transformations and ETL, save your data as tables and parquet files, read from these sources, and analyze datasets using DataFrames/Datasets API and Spark SQL. Level: Beginner to intermediate, not for advanced Spark users. Prerequisite: You will need a laptop with Chrome or Firefox browser installed with at least 8 GB. Introductory or basic knowledge Scala or Python is required, since the Notebooks will be in Scala; Python is optional. Bio: Jules S. Damji is an Apache Spark Community Evangelist with Databricks. He is a hands-on developer with over 15 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, LoudCloud/Opsware, VeriSign, Scalix, and ProQuest, building large-scale distributed systems. Before joining Databricks, he was a Developer Advocate at Hortonworks.

Jumpstart on Apache Spark 2.2 on Databricks

Jumpstart on Apache Spark 2.2 on Databricks

Jumpstart on Apache Spark 2.2 on Databricks

This introductory workshop is aimed at data analysts & data engineers new to Apache Spark and exposes them how to analyze big data with Spark SQL and DataFrames. In this partly instructor-led and self-paced labs, we will cover Spark concepts and you’ll do labs for Spark SQL and DataFrames in Databricks Community Edition. Toward the end, you’ll get a glimpse into newly minted Databricks Developer Certification for Apache Spark: what to expect & how to prepare for it. * Apache Spark Basics & Architecture * Spark SQL * DataFrames * Brief Overview of Databricks Certified Developer for Apache Spark

Spark Saturday: Spark SQL & DataFrame Workshop with Apache Spark 2.3

Spark Saturday: Spark SQL & DataFrame Workshop with Apache Spark 2.3

Spark Saturday: Spark SQL & DataFrame Workshop with Apache Spark 2.3

Apache Spark on HDinsight Training

Apache Spark on HDinsight Training

Apache Spark on HDinsight Training

Synergetics Learning and Cloud Consulting

Intro to Apache Spark

Intro to Apache Spark

Intro to Apache Spark

Intro to Apache Spark

Intro to Apache Spark

Intro to Apache Spark

Cassandra and Spark SQL

Cassandra and Spark SQL

Cassandra and Spark SQL

Russell Spitzer

Spark Introduction

Spark Introduction

Spark Introduction

DataStax Academy

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Spark Summit EU talk by Miklos Christine paddling up the stream

Spark Summit EU talk by Miklos Christine paddling up the stream

Paris Data Geek - Spark Streaming

Paris Data Geek - Spark Streaming

Paris Data Geek - Spark Streaming

Apache cassandra and spark. you got the the lighter, let's start the fire

Apache cassandra and spark. you got the the lighter, let's start the fire

Apache cassandra and spark. you got the the lighter, let's start the fire

Patrick McFadin

Nomad is a modern cluster manager by HashiCorp, designed for both long-lived services and short-lived batch processing workloads. The Nomad team has been working to bring a native integration between Nomad and Apache Spark. By running Spark jobs on Nomad, both Spark developers and the engineering organization benefit. Nomad’s architecture allows it to have an incredibly high scheduling throughput. To demonstrate this, HashiCorp scheduled 1 million containers in less than five minutes. That speed means that large Spark workloads can be immediately placed, minimizing job runtime and job start latencies. For an organization, Nomad offers many benefits. Since Nomad was designed for both batch and services, a single cluster can service both an organization’s Spark workload and all service-oriented jobs. That, coupled with the fact that Nomad uses bin-packing to place multiple jobs on each machine, means that organizations can achieve higher density. Which saves money and makes capacity planning easier. In the future, Nomad will also have the ability to enforce quotas and apply chargebacks, allowing multi-tenant clusters to be easily managed. To further increase the performance of Spark on Nomad, HashiCorp would like to ingest HDFS locality information to place the compute by the data.

Homologous Apache Spark Clusters Using Nomad with Alex Dadgar

Homologous Apache Spark Clusters Using Nomad with Alex Dadgar

Homologous Apache Spark Clusters Using Nomad with Alex Dadgar

We are a company driven by inquisitive data scientists, having developed a pragmatic and interdisciplinary approach, which has evolved over the decades working with over 100 clients across multiple industries. Combining several Data Science techniques from statistics, machine learning, deep learning, decision science, cognitive science, and business intelligence, with our ecosystem of technology platforms, we have produced unprecedented solutions. Welcome to the Data Science Analytics team that can do it all, from architecture to algorithms. Our practice delivers data driven solutions, including Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics. We employ a number of technologies in the area of Big Data and Advanced Analytics such as DataStax (Cassandra), Databricks (Spark), Cloudera, Hortonworks, MapR, R, SAS, Matlab, SPSS and Advanced Data Visualizations. This presentation is designed for Spark Enthusiasts to get started and details of the course are below. 1. Introduction to Apache Spark 2. Functional Programming + Scala 3. Spark Core 4. Spark SQL + Parquet 5. Advanced Libraries 6. Tips & Tricks 7. Where do I go from here?

Introduction to Spark - DataFactZ

Introduction to Spark - DataFactZ

Introduction to Spark - DataFactZ

In these slides is given an overview of the different parts of Apache Spark. We analyze spark shell both in scala and python. Then we consider Spark SQL with an introduction to Data Frame API. Finally we describe Spark Streaming and we make some code examples. Topics:spark-shell, pyspark, HDFS, how to copy file to HDFS, spark transformations, spark actions, Spark SQL (Shark), spark streaming, streaming transformation stateless vs stateful, sliding windows, examples

11. From Hadoop to Spark 2/2

11. From Hadoop to Spark 2/2

11. From Hadoop to Spark 2/2

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Spark Summit East 2015 Advanced Devops Student Slides

Spark Summit East 2015 Advanced Devops Student Slides

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Migrating ETL Workflow to Apache Spark at Scale in Pinterest

Migrating ETL Workflow to Apache Spark at Scale in Pinterest

Migrating ETL Workflow to Apache Spark at Scale in Pinterest

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Apache Spark and DataStax Enablement

Apache Spark and DataStax Enablement

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5 Ways to Use Spark to Enrich your Cassandra Environment

5 Ways to Use Spark to Enrich your Cassandra Environment

Global Big Data Conference Sept 2014 AWS Kinesis Spark Streaming Approximatio...

Global Big Data Conference Sept 2014 AWS Kinesis Spark Streaming Approximatio...

Global Big Data Conference Sept 2014 AWS Kinesis Spark Streaming Approximatio...

A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...

A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...

A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...

Jump Start on Apache® Spark™ 2.x with Databricks

Jump Start on Apache® Spark™ 2.x with Databricks

Jump Start on Apache® Spark™ 2.x with Databricks

Jumpstart on Apache Spark 2.2 on Databricks

Jumpstart on Apache Spark 2.2 on Databricks

Jumpstart on Apache Spark 2.2 on Databricks

Spark Saturday: Spark SQL & DataFrame Workshop with Apache Spark 2.3

Spark Saturday: Spark SQL & DataFrame Workshop with Apache Spark 2.3

Spark Saturday: Spark SQL & DataFrame Workshop with Apache Spark 2.3

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Apache Spark on HDinsight Training

Apache Spark on HDinsight Training

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Intro to Apache Spark

Intro to Apache Spark

Intro to Apache Spark

Intro to Apache Spark

Intro to Apache Spark

Cassandra and Spark SQL

Cassandra and Spark SQL

Cassandra and Spark SQL

Spark Introduction

Spark Introduction

Spark Introduction

Spark Summit EU talk by Miklos Christine paddling up the stream

Spark Summit EU talk by Miklos Christine paddling up the stream

Spark Summit EU talk by Miklos Christine paddling up the stream

Paris Data Geek - Spark Streaming

Paris Data Geek - Spark Streaming

Paris Data Geek - Spark Streaming

Apache cassandra and spark. you got the the lighter, let's start the fire

Apache cassandra and spark. you got the the lighter, let's start the fire

Apache cassandra and spark. you got the the lighter, let's start the fire

Homologous Apache Spark Clusters Using Nomad with Alex Dadgar

Homologous Apache Spark Clusters Using Nomad with Alex Dadgar

Homologous Apache Spark Clusters Using Nomad with Alex Dadgar

Introduction to Spark - DataFactZ

Introduction to Spark - DataFactZ

Introduction to Spark - DataFactZ

11. From Hadoop to Spark 2/2

11. From Hadoop to Spark 2/2

11. From Hadoop to Spark 2/2

Spark Summit East 2015 Advanced Devops Student Slides

Spark Summit East 2015 Advanced Devops Student Slides

Spark Summit East 2015 Advanced Devops Student Slides

Plus de Vasil Remeniuk

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Product Minsk - РТБ и Программатик

Product Minsk - РТБ и Программатик

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Работа с Akka Сluster, @afiskon, scalaby#14

Работа с Akka Сluster, @afiskon, scalaby#14

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Cake pattern. Presentation by Alex Famin at scalaby#14

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Scala laboratory: Globus. iteration #3

Scala laboratory: Globus. iteration #3

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Testing in Scala by Adform research

Testing in Scala by Adform research

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Spark Intro by Adform Research

Spark Intro by Adform Research

Types by Adform Research, Saulius Valatka

Types by Adform Research, Saulius Valatka

Types by Adform Research, Saulius Valatka

Types by Adform Research

Types by Adform Research

Types by Adform Research

Scalding by Adform Research, Alex Gryzlov

Scalding by Adform Research, Alex Gryzlov

Scalding by Adform Research, Alex Gryzlov

Scalding by Adform Research, Alex Gryzlov

Scalding by Adform Research, Alex Gryzlov

Scalding by Adform Research, Alex Gryzlov

Spark by Adform Research, Paulius

Spark by Adform Research, Paulius

Spark by Adform Research, Paulius

Scala Style by Adform Research (Saulius Valatka)

Scala Style by Adform Research (Saulius Valatka)

Scala Style by Adform Research (Saulius Valatka)

Spark intro by Adform Research

Spark intro by Adform Research

Spark intro by Adform Research

SBT by Aform Research, Saulius Valatka

SBT by Aform Research, Saulius Valatka

SBT by Aform Research, Saulius Valatka

Scala laboratory: Globus. iteration #2

Scala laboratory: Globus. iteration #2

Scala laboratory: Globus. iteration #2

Testing in Scala. Adform Research

Testing in Scala. Adform Research

Testing in Scala. Adform Research

Scala laboratory. Globus. iteration #1

Scala laboratory. Globus. iteration #1

Scala laboratory. Globus. iteration #1

Опыт использования Spark, Основано на реальных событиях

Опыт использования Spark, Основано на реальных событиях

Опыт использования Spark, Основано на реальных событиях

ETL со Spark

Funtional Reactive Programming with Examples in Scala + GWT

Funtional Reactive Programming with Examples in Scala + GWT

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Product Minsk - РТБ и Программатик

Product Minsk - РТБ и Программатик

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Работа с Akka Сluster, @afiskon, scalaby#14

Работа с Akka Сluster, @afiskon, scalaby#14

Cake pattern. Presentation by Alex Famin at scalaby#14

Cake pattern. Presentation by Alex Famin at scalaby#14

Cake pattern. Presentation by Alex Famin at scalaby#14

Scala laboratory: Globus. iteration #3

Scala laboratory: Globus. iteration #3

Scala laboratory: Globus. iteration #3

Testing in Scala by Adform research

Testing in Scala by Adform research

Testing in Scala by Adform research

Spark Intro by Adform Research

Spark Intro by Adform Research

Spark Intro by Adform Research

Types by Adform Research, Saulius Valatka

Types by Adform Research, Saulius Valatka

Types by Adform Research, Saulius Valatka

Types by Adform Research

Types by Adform Research

Types by Adform Research

Scalding by Adform Research, Alex Gryzlov

Scalding by Adform Research, Alex Gryzlov

Scalding by Adform Research, Alex Gryzlov

Scalding by Adform Research, Alex Gryzlov

Scalding by Adform Research, Alex Gryzlov

Scalding by Adform Research, Alex Gryzlov

Spark by Adform Research, Paulius

Spark by Adform Research, Paulius

Spark by Adform Research, Paulius

Scala Style by Adform Research (Saulius Valatka)

Scala Style by Adform Research (Saulius Valatka)

Scala Style by Adform Research (Saulius Valatka)

Spark intro by Adform Research

Spark intro by Adform Research

Spark intro by Adform Research

SBT by Aform Research, Saulius Valatka

SBT by Aform Research, Saulius Valatka

SBT by Aform Research, Saulius Valatka

Scala laboratory: Globus. iteration #2

Scala laboratory: Globus. iteration #2

Scala laboratory: Globus. iteration #2

Testing in Scala. Adform Research

Testing in Scala. Adform Research

Testing in Scala. Adform Research

Scala laboratory. Globus. iteration #1

Scala laboratory. Globus. iteration #1

Scala laboratory. Globus. iteration #1

Опыт использования Spark, Основано на реальных событиях

Опыт использования Spark, Основано на реальных событиях

Опыт использования Spark, Основано на реальных событиях

ETL со Spark

Funtional Reactive Programming with Examples in Scala + GWT

Funtional Reactive Programming with Examples in Scala + GWT

Funtional Reactive Programming with Examples in Scala + GWT

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Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the deployment of external web forms using Jotform for Bonterra Impact Management. This solution can be customized to your organization’s needs and deployed to support the common use cases below: - Intake and consent - Assessments - Surveys - Applications - Program registration Interested in deploying web form automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.

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presentation ICT roal in 21st century education

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Real Time Object Detection Using Open CV

Real Time Object Detection Using Open CV

Real Time Object Detection Using Open CV

This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.

Powerful Google developer tools for immediate impact! (2023-24 C)

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2024: Domino Containers - The Next Step. News from the Domino Container commu...

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As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape

TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments

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Manulife - Insurer Transformation Award 2024

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"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

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Scalable LLM APIs for AI and Generative AI Application Development Ettikan Karuppiah, Director/Technologist - NVIDIA Apidays Singapore 2024: Connecting Customers, Business and Technology (April 17 & 18, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays Singapore 2024 - Scalable LLM APIs for AI and Generative AI Applicati...

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Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

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AWS Community Day CPH - Three problems of Terraform

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Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

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Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

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presentation ICT roal in 21st century education

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Real Time Object Detection Using Open CV

Real Time Object Detection Using Open CV

Real Time Object Detection Using Open CV

Powerful Google developer tools for immediate impact! (2023-24 C)

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Artificial Intelligence Chap.5 : Uncertainty

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2024: Domino Containers - The Next Step. News from the Domino Container commu...

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TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments

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Manulife - Insurer Transformation Award 2024

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AXA XL - Insurer Innovation Award Americas 2024

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EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER

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Cassandra + Spark + Elk

1. Cassandra+Spark+ELK Dmitriy Kalyada @ 2015

2. What is Spark? • Master: Driver program • Workers: Executors • High Availability • Standby Masters with ZooKeeper • Single-Node Recovery with Local File System

3. Under the hood • Resilient Distributed Dataset (RDD) • Scala + Akka Framework • Java, Scala, Python API • Spark SQL, MLib, Spark Streaming, GraphX

4. Our particular case Devices Cassandra Spark ELK

5. Data ﬂow Fetcher Transformer Saver Input Source(s) x-RDD x-RDD Output Source

6. Spark Cassandra Connector • Represents Cassandra tables as Spark RDDs • Write Spark RDDs to Cassandra tables • Execute CQL queries in Spark applications https://github.com/datastax/spark-cassandra-connector

7. CassandraRDD settings • Connection params • Fetching params 1. input.split.size: C* partitions in a Spark Partition. 2. input.page.row.size: number of CQL rows fetched per roundtrip.

8. Fetching essentials … … … … -968391295277638458 … -893783532241185833 -968391295277638458, -893783532241185833 -7378580094811526501, -7340240117176401239 6426215139012569257, 6428979455828914106 -6094480671546553265, -6016282219056649738 -7259249675596554667, -7237838231745167324 -6734336817058726139, -6684208157211348972 -3891103372671105499, -3822513456325086923 4453206019575747361,4462441725813855391 7855385326468991461,7906589648045207141 -129433796439502583,-101280166181350027 -2233788032218452383,-2066644620711092198 3248662132571799756,3396129453515776704 7744134136205124749,7812918342246679728 -1408208314239486033,-1403736406052004344 • Support Murmur3Partitioner and RandomPartitioner • Retrieve token ranges from Cassandra • Prediction on base of 16 random token ranges

9. Data to RDD … Tokens Per RDD [input.split.size] Token Range #N Slurp amount [input.page.row.size]

10. Token range vs rows number

11. What to do? • Change read strategy • Split data on a smaller pieces • Increase cluster strength • Reorganize Cassandra schema

12. Elastic Search

13. Elastic Search & Kibana • Index initialization: TransportClient • Create/Delete Index • Setup Mappings • Indexing: ScalaEsRDD • Data presentation: Kibana

15. Deployment • Build package: Spark Job + Dependent Jars + Conﬁgs • Upload to the Spark Master Node • Start job submit script

16. Thank you dkaliada@exadel.com Dmitriy Kalyada @ 2015