Voldemort is a distributed key-value store inspired by Dynamo and developed by LinkedIn as open source. It provides a simple get, put, delete API and can store values in various formats including JSON, protobuf, and Avro. Voldemort uses consistent hashing to partition and replicate data across multiple servers and provides high availability and performance for read/write workloads.
The document discusses queryable state for Apache Kafka Streams. It introduces Kafka Streams and stateful transformations. It then describes state for Kafka Streams, including how state is stored in RocksDB and tracked with a changelog in Kafka. Finally, it covers the new queryable state feature in Kafka Streams 0.10.1, which provides APIs to access state stores and retrieve values by key for windowed state.
Voldemort is a distributed key-value store inspired by Dynamo and developed by LinkedIn as open source. It provides a simple get, put, delete API and can store values in various formats including JSON, protobuf, and Avro. Voldemort uses consistent hashing to partition and replicate data across multiple servers and provides high availability and performance for read/write workloads.
The document discusses queryable state for Apache Kafka Streams. It introduces Kafka Streams and stateful transformations. It then describes state for Kafka Streams, including how state is stored in RocksDB and tracked with a changelog in Kafka. Finally, it covers the new queryable state feature in Kafka Streams 0.10.1, which provides APIs to access state stores and retrieve values by key for windowed state.
Spark Streaming allows processing of live data streams using Spark. It works by dividing the data stream into batches called micro-batches, which are then processed using Spark's batch engine to generate RDDs. This allows for fault tolerance, exactly-once processing, and integration with other Spark APIs like MLlib and GraphX.
This document compares Apache Kafka and AWS Kinesis for message streaming. It outlines that Kafka is an open source publish-subscribe messaging system designed as a distributed commit log, while Kinesis provides streaming data services. It also notes some key differences like Kafka typically handling over 8000 messages/second while Kinesis can handle under 100 messages/second.
This document discusses messaging queues and platforms. It begins with an introduction to messaging queues and their core components. It then provides a table comparing 8 popular open source messaging platforms: Apache Kafka, ActiveMQ, RabbitMQ, NATS, NSQ, Redis, ZeroMQ, and Nanomsg. The document discusses using Apache Kafka for streaming and integration with Google Pub/Sub, Dataflow, and BigQuery. It also covers benchmark testing of these platforms, comparing throughput and latency. Finally, it emphasizes that messaging queues can help applications by allowing producers and consumers to communicate asynchronously.
- Apache Spark is an open-source cluster computing framework for large-scale data processing. It was originally developed at the University of California, Berkeley in 2009 and is used for distributed tasks like data mining, streaming and machine learning.
- Spark utilizes in-memory computing to optimize performance. It keeps data in memory across tasks to allow for faster analytics compared to disk-based computing. Spark also supports caching data in memory to optimize repeated computations.
- Proper configuration of Spark's memory options is important to avoid out of memory errors. Options like storage fraction, execution fraction, on-heap memory size and off-heap memory size control how Spark allocates and uses memory across executors.
Proposal of Information Recommendation Method that Diversity in Consideration of the Topic
DEIM2014に投稿した論文についてB8:情報推薦(1)セッションで発表させていただきました.
プレゼンテーションで使用したスライドになります.
Spark Streaming allows processing of live data streams using Spark. It works by dividing the data stream into batches called micro-batches, which are then processed using Spark's batch engine to generate RDDs. This allows for fault tolerance, exactly-once processing, and integration with other Spark APIs like MLlib and GraphX.
This document compares Apache Kafka and AWS Kinesis for message streaming. It outlines that Kafka is an open source publish-subscribe messaging system designed as a distributed commit log, while Kinesis provides streaming data services. It also notes some key differences like Kafka typically handling over 8000 messages/second while Kinesis can handle under 100 messages/second.
This document discusses messaging queues and platforms. It begins with an introduction to messaging queues and their core components. It then provides a table comparing 8 popular open source messaging platforms: Apache Kafka, ActiveMQ, RabbitMQ, NATS, NSQ, Redis, ZeroMQ, and Nanomsg. The document discusses using Apache Kafka for streaming and integration with Google Pub/Sub, Dataflow, and BigQuery. It also covers benchmark testing of these platforms, comparing throughput and latency. Finally, it emphasizes that messaging queues can help applications by allowing producers and consumers to communicate asynchronously.
- Apache Spark is an open-source cluster computing framework for large-scale data processing. It was originally developed at the University of California, Berkeley in 2009 and is used for distributed tasks like data mining, streaming and machine learning.
- Spark utilizes in-memory computing to optimize performance. It keeps data in memory across tasks to allow for faster analytics compared to disk-based computing. Spark also supports caching data in memory to optimize repeated computations.
- Proper configuration of Spark's memory options is important to avoid out of memory errors. Options like storage fraction, execution fraction, on-heap memory size and off-heap memory size control how Spark allocates and uses memory across executors.
Proposal of Information Recommendation Method that Diversity in Consideration of the Topic
DEIM2014に投稿した論文についてB8:情報推薦(1)セッションで発表させていただきました.
プレゼンテーションで使用したスライドになります.
1. Machine Learning
for Natural Language Processing
- Way of Experiment & Evaluation –
Meiji University
Seminar 2 B3 Tatsuya Coike
Web : lanevok.com
pp. 162-178
2012.10.11 (Thu)
4. Way of Experiment & Evaluation 4
1.1 実験 の 準備
実験プログラムとデータの入手
• WEKA (Data Mining with Open Source
Machine Learning Software in Java)
• README (Input Format)
• Data Set (p.185)
5. Way of Experiment & Evaluation 5
1.2 データセット
• 事例 (Instance)
• Data Set の 事例数 (= Data Size)
※ データサイズ ≠ データ数 分類
新聞 Wikipedia 事例数 3
データ数 2
記事 (Data Size)
Data Set A
Data Set B
図 1.2 データサイズとデータ数
7. Way of Experiment & Evaluation 7
1.4 交差検定
実験→
• Cross-Validation
Data Set A1 評価 訓練 訓練
Data Set A2 訓練 評価 訓練
分割
Data Set A
Data Set A3 訓練 訓練 評価
図 1.4 交差検定
8. Way of Experiment & Evaluation 8
1.5 クラス と ラベル
赤
赤でない
赤
Class O
青
Class X Class R
分類
Class B
不明
分類
黒
Class E
りんご Class K
りんご 赤
Data Set A
青
Data Set B
図 1.5.1 クラス
図 1.5.2 ラベル
9. Way of Experiment & Evaluation 9
1.6 分類
二値分類問題 と 多値分類問題
Class > 2 Class = 2
• Class 多クラスデータセット 二値クラスデータセット
(Multi-Class Dataset) (Binary-Class Dataset)
Label > 1 Class = 1
• Label 複数ラベルデータセット 単一ラベルデータセット
(Multi-Label Dataset) (Single-Label Dataset)