掌握Apache Spark 2.0
Introduction
Overview of Apache Spark
Spark SQL
Spark SQL — Structured Queries on Large Scale
SparkSession — The Entry Point to Spark SQL
- Builder — Building SparkSession with Fluent API
Datasets — Strongly-Typed DataFrames with Encoders
- Encoders — Internal Row Converters
- InternalRow — Internal Binary Row Format
DataFrame — Dataset of Rows
- Row
- RowEncoder — DataFrame Encoder
Schema — Structure of Data
Dataset Operators
DataSource API — Loading and Saving Datasets
BaseRelation
SparkPlanner — Query Planner
Structured Query Plan
Debugging Query Execution
Datasets vs DataFrames vs RDDs
SQLConf
Catalog
ExternalCatalog — System Catalog of Permanent Entities
SessionState
SQL Parser Framework
SQLExecution Helper Object
Logical Query Plan Optimizer
CatalystSerde
Tungsten Execution Backend (aka Project Tungsten)
- Whole-Stage Code Generation (CodeGen)
Hive Integration
- Spark SQL CLI - spark-sql
CacheManager
Thrift JDBC/ODBC Server — Spark Thrift Server (STS)
- SparkSQLEnv
Catalyst — Tree Manipulation Framework
(obsolete) SQLContext
Settings
Spark MLlib
Spark MLlib — Machine Learning in Spark
ML Pipelines and PipelineStages (spark.ml)
Latent Dirichlet Allocation (LDA)
Vector
LabeledPoint
Streaming MLlib
GeneralizedLinearRegression
Structured Streaming
Structured Streaming — Streaming Datasets
DataStreamReader
DataStreamWriter
Streaming Sources
Streaming Sinks
- ConsoleSink
- ForeachSink
StreamSourceProvider — Streaming Source Provider
- KafkaSourceProvider
- TextSocketSourceProvider
StreamSinkProvider
StreamingQueryManager
StreamingQuery
Trigger
StreamExecution
StreamingRelation
StreamingQueryListenerBus
MemoryPlan Logical Query Plan
Spark Streaming
Spark Streaming
StreamingContext
web UI and Streaming Statistics Page
Streaming Listeners
Checkpointing
JobScheduler
- InputInfoTracker
JobGenerator
DStreamGraph
Discretized Streams (DStreams)
Receivers
Ingesting Data from Kafka
RecurringTimer
Backpressure
Dynamic Allocation (Elastic Scaling)
- ExecutorAllocationManager
StreamingSource
Settings
Spark Core / Tools
Spark Shell — spark-shell shell script
Web UI — Spark Application’s Web Console
Spark Submit — spark-submit shell script
spark-class shell script
- AbstractCommandBuilder
SparkLauncher — Launching Spark Applications Programmatically
Spark Core / Architecture
Spark Architecture
Driver
Executors
- TaskRunner
- ExecutorSource
Master
Workers
Spark Core / RDD
Anatomy of Spark Application
SparkConf — Programmable Configuration for Spark Applications
- Spark Properties and spark-defaults.conf Properties File
- Deploy Mode
SparkContext
RDD - Resilient Distributed Dataset
Spark Core / Optimizations
Caching and Persistence
Broadcast variables
Accumulators
Spark Core / Services
SerializerManager
MemoryManager — Memory Management
- UnifiedMemoryManager
SparkEnv — Spark Runtime Environment
DAGScheduler
Task Scheduler
Scheduler Backend
- CoarseGrainedSchedulerBackend
Executor Backend
- CoarseGrainedExecutorBackend
BlockManager
Dynamic Allocation (of Executors)
Shuffle Manager
- ExternalShuffleService
ExternalClusterManager — Pluggable Cluster Managers
HTTP File Server
Broadcast Manager
Data Locality
Cache Manager
Spark, Akka and Netty
OutputCommitCoordinator
RPC Environment (RpcEnv)
- Netty-based RpcEnv
ContextCleaner
MapOutputTracker
- MapOutputTrackerMaster
TransportConf — Transport Configuration
Spark Deployment Environments
Deployment Environments — Run Modes
Spark local (pseudo-cluster)
Spark on cluster
Spark on YARN
Spark on YARN
YarnShuffleService — ExternalShuffleService on YARN
ExecutorRunnable
Client
YarnRMClient
ApplicationMaster
- AMEndpoint — ApplicationMaster RPC Endpoint
YarnClusterManager — ExternalClusterManager for YARN
TaskSchedulers for YARN
- YarnScheduler
- YarnClusterScheduler
SchedulerBackends for YARN
YarnAllocator
Introduction to Hadoop YARN
Setting up YARN Cluster
Kerberos
- ConfigurableCredentialManager
ClientDistributedCacheManager
YarnSparkHadoopUtil
Settings
Spark Standalone
Spark Standalone
Standalone Master
Standalone Worker
web UI
Submission Gateways
Management Scripts for Standalone Master
Management Scripts for Standalone Workers
Checking Status
Example 2-workers-on-1-node Standalone Cluster (one executor per worker)
StandaloneSchedulerBackend
Spark on Mesos
Spark on Mesos
MesosCoarseGrainedSchedulerBackend
About Mesos
Execution Model
Execution Model
Security
Spark Security
Securing Web UI
Spark Core / Data Sources
Data Sources in Spark
Using Input and Output (I/O)
- Spark and Parquet
- Serialization
Spark and Cassandra
Spark and Kafka
Couchbase Spark Connector
Spark GraphX
Spark GraphX — Distributed Graph Computations
Graph Algorithms
Monitoring, Tuning and Debugging
Unified Memory Management
Spark History Server
Logging
Performance Tuning
Spark Metrics System
- MetricsConfig — Metrics System Configuration
- Metrics Source
Spark Listeners — Intercepting Events from Spark Scheduler
Debugging Spark using sbt
Varia
Building Apache Spark from Sources
Spark and Hadoop
Spark and software in-memory file systems
Spark and The Others
Distributed Deep Learning on Spark
Spark Packages
Interactive Notebooks
Interactive Notebooks
- Apache Zeppelin
- Spark Notebook
Spark Tips and Tricks
Spark Tips and Tricks
Access private members in Scala in Spark shell
SparkException: Task not serializable
Running Spark on Windows
Exercises
One-liners using PairRDDFunctions
Learning Jobs and Partitions Using take Action
Spark Standalone - Using ZooKeeper for High-Availability of Master
Spark’s Hello World using Spark shell and Scala
WordCount using Spark shell
Your first complete Spark application (using Scala and sbt)
Spark (notable) use cases
Using Spark SQL to update data in Hive using ORC files
Developing Custom SparkListener to monitor DAGScheduler in Scala
Developing RPC Environment
Developing Custom RDD
Working with Datasets using JDBC (and PostgreSQL)
Causing Stage to Fail
Further Learning
Courses
Books
Spark Distributions
DataStax Enterprise
MapR Sandbox for Hadoop (Spark 1.5.2 only)
Spark Workshop
Spark Advanced Workshop
- Requirements
- Day 1
- Day 2
Spark Talk Ideas
Spark Talks Ideas (STI)
10 Lesser-Known Tidbits about Spark Standalone
Learning Spark internals using groupBy (to cause shuffle)

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