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.
CTF for ビギナーズのネットワーク講習で使用した資料です。
講習に使用したファイルは、以下のリンク先にあります。
https://onedrive.live.com/redir?resid=5EC2715BAF0C5F2B!10056&authkey=!ANE0wqC_trouhy0&ithint=folder%2czip
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.
CTF for ビギナーズのネットワーク講習で使用した資料です。
講習に使用したファイルは、以下のリンク先にあります。
https://onedrive.live.com/redir?resid=5EC2715BAF0C5F2B!10056&authkey=!ANE0wqC_trouhy0&ithint=folder%2czip
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.
True to its name, Ananta provides cloud scale load balancing. It addresses limitations of traditional load balancers by supporting 100Gbps per VIP, rapid failover of thousands of VIPs, and tenant isolation to prevent overloads in one tenant from impacting others. Ananta implements load balancing across three tiers - packet-level in routers, connection-level in servers, and stateful NAT - to achieve high scalability, availability, and flexibility.
- 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.
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.
True to its name, Ananta provides cloud scale load balancing. It addresses limitations of traditional load balancers by supporting 100Gbps per VIP, rapid failover of thousands of VIPs, and tenant isolation to prevent overloads in one tenant from impacting others. Ananta implements load balancing across three tiers - packet-level in routers, connection-level in servers, and stateful NAT - to achieve high scalability, availability, and flexibility.
- 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.