Index
A
B
C
- clustering model
- clustering synthetic control data use case
- cluster setup requisites
- cluster statistics
- combine(T val1, T val2) method / The CombinerAggregator interface
- CombinerAggregator<T> interface
- CombinerAggregator interface / The CombinerAggregator interface
- complete(State state, TridentCollector tridentCollector) method / The Aggregator interface
- components, Ganglia
- components, Hadoop cluster
- components, HDFS
- components, Storm
- components, Storm topology
- components, topology
- components, YARN cluster
- consumer
- count field
- custom grouping
D
- data model, Storm
- DataNode / Understanding HDFS
- data retention
- development environment setup
- development machine
- direct grouping
- Distributed RPC
E
- edit logs
- execute() method
- executor
F
- features, Storm
- fields grouping
G
H
- Hadoop
- Hadoop 2.2.0
- Hadoop Common
- HBase
- HBase installation
- HBaseOperations class
- HDFS
- HDFS client / Understanding HDFS
- hdfs dfs command / Setting up HDFS
- Hello World topology
I
J
K
- Kafka
- Kafka architecture
- Kafka spout
- Kafka spout integration
- Kafka topic distribution / Replication
- keyword
L
- LearningStormClusterTopology
- local or shuffle grouping
- log-processing topology
- logfile
- log processing
M
- machine-learning algorithms, Storm applications / Using Trident-ML
- machine learning
- MapGet() function / Distributed RPC
- Maven
- MemoryMapState.Factory() method / Distributed RPC
- message processing
- methods, bolt
- methods, spout
- monitoring
- multiple Kafka brokers
- multivariate control chart
- MySQL queries
N
O
- offline learning
- offset
- online learning
- opaque transactional spout
- opaque transactional topology
- operation modes, Storm topology
P
- parallelism, sample topology
- parallelism, Storm topology
- partition aggregate / The partition aggregate
- partitionAggregate function
- partitionBy operation
- partition operation
- password-less SSH
- PATH variable / Installing Java SDK 6
- persistent aggregate / The persistent aggregate
- persistentAggregate function / Distributed RPC
- prepare() method
- process data
- processing semantics
- producer
- properties, server.properties file
R
S
- sample Kafka producer
- sample topology
- developing / Developing a sample topology
- deploying, on remote Storm cluster / Deploying the sample topology on the remote cluster
- worker processes, distributing / Distributing worker processes, executors, and tasks in the sample topology
- tasks, distributing / Distributing worker processes, executors, and tasks in the sample topology
- executors, distributing / Distributing worker processes, executors, and tasks in the sample topology
- Secondary NameNode / Understanding HDFS
- server log line
- shuffle grouping
- shuffle operation
- single-node cluster
- single-node Kafka cluster
- single-node ZooKeeper instance
- single node
- Split function / Distributed RPC
- spout
- SpoutStatistics class / Fetching information with the Nimbus thrift client
- stateQuery() method / Distributed RPC
- statistics, LearningStormClusterTopology
- Storm
- Storm-Starter topologies
- Storm-YARN
- Storm client
- Storm cluster
- Storm home page / Starting to use the Storm UI
- Storm topology
- Storm UI
- Storm UI daemon
- stream
- stream grouping
- STS
- STS IDE
- supervisor nodes
- SupervisorStatistics class / Fetching information with the Nimbus thrift client
T
- task
- three-node Storm cluster
- three node Kafka cluster
- ThriftClient class / Fetching information with the Nimbus thrift client
- topics
- topology
- topology state
- training
- training dataset
- transactional topology
- transaction spout implementation
- Trident
- Trident's filter
- Trident's function
- Trident's projection
- Trident's sample topology
- Trident-ML
- Trident data model
- Trident repartitioning operations
- Trident topology
- TridentTuple interface
- tuple
U
- UCI Machine Learning Repository
- univariate control chart
V
W
Y
Z
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