Streams: a new general purpose data structure in Redis. - <antirez>
Until a few months ago, for me streams were no more than an interesting and relatively straightforward concept in the context of messaging. After Kafka popularized the concept, I mostly investigated their usefulness in the case of Disque, a message queue that is now headed to be translated into a Redis 4.2 module. Later I decided that Disque was all about AP messaging, which is, fault tolerance an
Common SQL clauses and their equivalents in Streams
Functional programming allows for quasi-declarative programming in a general purpose language. By using powerful fluent APIs like Java 8’s Stream API, or jOOλ’s sequential Stream extension Seq or more sophisticated libraries like vavr or functionaljava, we can express data transformation algorithms in an extremely concise way. Compare Mario Fusco’s imperative and functional version of the same alg
little code, much immutability: java 8 streams and immutable collections – MetaBroadcast
we write about the things we build and the things we consume I finally got to play with Java 8 streams and lambdas in more than a “Hello, World” way a couple of weeks ago. It was all pretty basic stuff—a map here, a few filters there, a sprinkling of counts—but it’s lovely to finally have first-class support for things we’ve been using in Guava for years, and lambdas make the syntax much more ters
Bottled Water: Real-time integration of PostgreSQL and Kafka | Confluent
Summary: Confluent is starting to explore the integration of databases with event streams. As part of the first step in this exploration, Martin Kleppmann has made a new open source tool called Bottled Water. It lets you transform your PostgreSQL database into a stream of structured Kafka events. This is tremendously useful for data integration. Writing to a database is easy, but getting the data
AWS News Blog
Stop the CNAME chain struggle: Simplified management with Route 53 Resolver DNS Firewall Starting today, you can configure your DNS Firewall to automatically trust all domains in a resolution chain (such as aCNAME, DNAME, or Alias chain). Let’s walk through this in nontechnical terms for those unfamiliar with DNS. Why use DNS Firewall? DNS Firewall provides protection for outbound DNS requests fro
Java 8 Friday: No More Need for ORMs
Every Friday, we’re showing you a couple of nice new tutorial-style Java 8 features, which take advantage of lambda expressions, extension methods, and other great stuff. You’ll find the source code on GitHub. Debates about the usefulness of ORM (Object-Relational Mapping) have been going on for the last decade. While many people would agree that Hibernate and JPA solve a lot of problems very well
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Mencari slot gacor hari ini? Anda berada di tempat yang tepat! Dalam dunia perjudian online, istilah "gacor" sering digunakan untuk menggambarkan slot yang sedang "panas" atau memiliki peluang tinggi untuk memberikan kemenangan besar kepada pemain. Dalam artikel panjang ini, kita akan membahas segala sesuatu tentang slot gacor: cara menemukannya, strategi untuk bermain, dan tentu saja, rekomendasi
StreamInsight - イベント ストリームを使いこなす: 高速概算集計
図 4 ヒストグラム (図 3) の図解 最初のイベントでは動きがありません。5 個目のイベントで同じカウント 1 を含むバケット数が k⁄2 + 2 個になるため、最も古いバケットのマージが行われます。7 個目のイベントでも再びマージが行われます。9 個目のイベントでもマージが行われますが、このマージによって新たなマージが連鎖的に引き起こされます。7 個目のイベントの後、最初のイベントの期限が切れます。12 個目のイベントまでは、タイムスタンプが期限切れになるバケットはありません。13 個目のイベントで、タイムスタンプ 6 のバケットは、期限が切れていないイベントを最低でも 1 つ含むという条件が成立しなくなるため、バケット自体が期限切れになります。この表の相対誤差は、相対誤差の上限より小さいことは明らかです。 図 4 の点線で囲んだ範囲はその時点のウィンドウ サイズです。ウィンドウは複
In-Stream Big Data Processing
The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. It became clear that real-time query processing and in-stream processing is the immediate need in many practical applications. In recent years, this idea got a lot of traction and a whole bunch of solutions like Twitter’s Storm, Yahoo’s S4, Cloudera’s Impala, Apa
Rocket
Rocket is a multi-purpose modular rocket control system. Among its features cross-platform support, a warhead plugin interface and support for fully standards-compliant remote navigation protocols. Rocket is managed and developed by the Rocket Software Foundation. The Rocket Software Foundation provides support for the Rocket community of open-source software projects. The Rocket projects are char
Esperwhispering
This document discusses using Esper, a complex event processing (CEP) engine, to analyze real-time web activity data and provide answers to questions about that data within seconds. It describes the large volume of event data involved, defines what types of information each event contains, and provides examples of Esper queries to calculate statistics, detect anomalies, and retrieve snapshots of q
学術的に見た ストリームデータ処理 2013年6月28日 筑波大学 講師 川島英之
4. STORM Norikra Jubatus CEP DSMS SPE Relational-stream XML-stream S4 STREAM System S Algorithm trading Borealis(MIT/Brandeis) Stream computing Complex event processing Online learning Incremental computation Continual query Spring (DTW) CPD (Change Point Detection) Window-aggregate Window-join FPGA GPU SASE Fraud detection Malware detection AQP (Adaptive Query Proc.) Esper BRIMOS Handshake-join I
理修棟生活 | ストリーム処理基盤
○モデル 従来のDBMSではデータの書き込み、牽引、問合せ、計算処理、結果の出力の流れとなっており、短い期間に数千のデータイベントを受信する場合ではこのデータベース駆動型アーキテクチャではデータストリームに対応が出来ない。 In-MemoryDBでも同じ。In-Memoryでは書き込みコストはなくなるが、インバウンドイベントの牽引、問合せ、トランザクションの完全性優位などにより、データストリーム対応は困難。 そこで、イベント駆動型アーキテクチャのストリーム処理エンジンが登場。 データ到着すると、即座に問合せ、計算処理を行い結果を出力し、データベースへの書き込みは必要なものだけをデータ処理と並行もしくは結果出力後行われる。これにより、real-time処理が出来る。 このモデル（アーキテクチャ）のために、スケジューラ、プロファイラ、オプティマイザ、エグゼキュータ、ロードシェダー、メモリマネー
Stream(SQL) Event Processing with Esper - igvita.com
By Ilya Grigorik on May 27, 2011 The growth of both the types and the amount of data generated by servers, users, and applications have resulted in a number of recent trends and innovations: NoSQL, rise of popularity of Hadoop, and dozens of higher-level map-reduce frameworks. However, the batch-processing model imposed by map-reduce style of processing is not always a great fit either, especially
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Java8 Stream APIとApache SparkとAsakusa Frameworkの類似点・相違点
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https://www.cac.co.jp/softechs/pdf/st3001_13.pdf