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文库首页行业研究行业报告藏经阁-Apache Kudu & Apache Spark SQL.pdf

藏经阁-Apache Kudu & Apache Spark SQL.pdf

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藏经阁-Apache Kudu & Apache Spark SQL.pdf

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Apache Kudu & Apache Spark SQL

for Fast Analytics on Fast Data

Mike Percy

Software Engineer at Cloudera

Apache Kudu PMC member

Kudu Overview

HDFS

Fast Scans, Analytics

and Processing of

Static Data

Fast On-Line

Updates &

Data Serving

Arbitrary Storage

(Active Archive)

Fast Analytics

(on fast-changing or

frequently-updated data)

Traditional Hadoop Storage Leaves a Gap

Use cases that fall between HDFS and HBase were difficult to manage

Unchanging

Fast Changing

Frequent Updates

HBase

Append-Only

Real-Time

Complex Hybrid

Architectures

Analytic

Gap

Pace of Analysis

Pace of Data

HDFS

Fast Scans, Analytics

and Processing of

Static Data

Fast On-Line

Updates &

Data Serving

Arbitrary Storage

(Active Archive)

Fast Analytics

(on fast-changing or

frequently-updated data)

Kudu: Fast Analytics on Fast-Changing Data

New storage engine enables new Hadoop use cases

Unchanging

Fast Changing

Frequent Updates

HBase

Append-Only

Real-Time

Kudu

Kudu fills the Gap

Modern analytic

applications often

require complex data

flow & difficult

integration work to

move data between

HBase & HDFS

Analytic

Gap

Pace of Analysis

Pace of Data

Apache Kudu: Scalable and fast tabular storage

Tabular

• Represents data in structured tables like a relational database

• Strict schema, finite column count, no BLOBs

• Individual record-level access to 100+ billion row tables

Scalable

• Tested up to 275 nodes (~3PB cluster)

• Designed to scale to 1000s of nodes and tens of PBs

Fast

• Millions of read/write operations per second across cluster

• Multiple GB/second read throughput per node

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