This document discusses the results of a NoSQL database benchmark test conducted by NHN on Cassandra, HBase and MongoDB. It describes the test environment and four test cases performed: data insertion, random reads and updates on existing data, reads only on existing data, and random reads and inserts with additional data added. The test measured average transactions per second for each database and test case. Cassandra and HBase performance varied depending on compaction levels while MongoDB update and insert performance lagged the others.
The document outlines a troubleshooting process for Cassandra, emphasizing the steps to diagnose problems, such as determining affected nodes, examining bottlenecks, and understanding errors. It provides insights into the tools and metrics available for monitoring performance, as well as logging configurations for troubleshooting. Additionally, it details various metrics related to latency and load, along with common causes of performance issues.
Fisl15 Streaming de video ao vivo na globo.comLeandro Moreira
油
O documento resume 10 li巽探es aprendidas ao transmitir v鱈deos ao vivo na globo.com. A principal li巽達o 辿 que o protocolo HLS 辿 muito melhor do que o RTMP para transmiss達o de v鱈deo, resultando em menos falhas de reprodu巽達o, melhor qualidade de v鱈deo e mais tempo assistido. Outra li巽達o 辿 a import但ncia crucial de medi巽達o e m辿tricas para otimizar o desempenho. Por fim, o documento defende iniciativas de c坦digo aberto para tornar o software mais gen辿rico e atrair contribui巽探es da comunidade.
This material is made to educate operators, who deal with cassandra in production environment and based on cassandra version 1.1.X
襭 Cassandra襯 蟆曙 伎蠍 , 伎襯 蟲 襭襦 Cassandra 1.1.X襯 蠍一朱 る 襭.
This document provides an overview of NoSQL databases. It begins by defining NoSQL as non-relational databases that are distributed, open source, and horizontally scalable. It then discusses some of the limitations of relational databases that led to the rise of NoSQL, such as issues with scalability and the need for flexible schemas. The document also summarizes some key NoSQL concepts, including the CAP theorem, ACID versus BASE, and eventual consistency. It provides examples of use cases for NoSQL databases and discusses some common NoSQL database types and how they address scalability.
The document provides a summary of the history and current state of big data and Hadoop. It discusses how the concept of big data emerged in the late 1990s and was further popularized by McKinsey in 2011. It then outlines the evolution of definitions and technologies around big data, including the development of Hadoop from the early 2000s onward. The document also analyzes current adoption trends, use cases, and market forecasts for big data and Hadoop.
This document summarizes several Cassandra anti-patterns including:
- Using a non-Oracle JVM which is not recommended.
- Putting the commit log and data directories on the same disk which can impact performance.
- Using EBS volumes on EC2 which can have unpredictable performance and throughput issues.
- Configuring overly large JVM heaps over 16GB which can cause garbage collection issues.
- Performing large batch mutations in a single operation which risks timeouts if not broken into smaller batches.
This document provides an introduction and overview of Couchbase Server, a NoSQL document database. It describes Couchbase Server as the leading open source project focused on distributed database technology. It outlines key features such as easy scalability, always-on availability, flexible data modeling using JSON documents, and core features including clustering, replication, indexing and querying. The document also provides examples of basic write, read and update operations on a single node and cluster, adding nodes, handling node failures, indexing and querying capabilities, and cross data center replication.
Cassandra is a distributed, decentralized, wide column store NoSQL database modeled after Amazon's Dynamo and Google's Bigtable. It provides high availability with no single point of failure, elastic scalability and tunable consistency. Cassandra uses consistent hashing to partition and distribute data across nodes, vector clocks to track data versions for consistency, and Merkle trees to detect and repair inconsistencies between replicas.
Cassandra nice use cases and worst anti patternsDuyhai Doan
油
This document discusses Cassandra use cases and anti-patterns. Some good use cases include rate limiting, fraud prevention, account validation, and storing sensor time series data. Poor designs include using Cassandra like a queue, storing null values, intensive updates to the same column, and dynamically changing the schema. The document provides examples and explanations of how to properly implement these scenarios in Cassandra.
strangeloop 2012 apache cassandra anti patternsMatthew Dennis
油
The document outlines various anti-patterns in using Apache Cassandra, emphasizing that it performs best on commodity hardware without SAN and detailing issues with oversized JVM heaps, incorrect file handle limits, and misconfigured caches. It highlights the importance of proper configuration, such as using initial tokens, avoiding large rows in caches, and not using certain features like BOP (batching of operations) improperly. Lastly, it suggests using OSS tools like OPS and avoiding non-standard JVMs for stability and compatibility.
This document discusses managing Apache Cassandra at scale. It provides an overview of Cassandra's history and evolution from Dynamo and BigTable. It also discusses Cassandra's data model and how it handles operations like reads, writes and updates in a distributed system without relying on read-modify-writes. The document also covers Cassandra best practices like using collections, lightweight transactions and time series data modeling to optimize for scalability.
Lessons Learned on Java Tuning for Our Cassandra Clusters (Carlos Monroy, Kne...DataStax
油
The document outlines Knewton's experience with JVM tuning for Cassandra clusters, highlighting challenges and successful strategies implemented to improve database performance. Key initiatives included updating memtable allocation settings, changing to garbage first garbage collection (G1GC), and enhancing monitoring tools. The findings emphasize the importance of testing configuration changes in collaboration with the development team to optimize performance and reduce technical debt.
Spark Streaming allows for scalable, high-throughput, fault-tolerant stream processing of live data streams. It works by chopping data streams into batches, treating each batch as an RDD, and processing them using RDD transformations and operations. This provides a simple abstraction called a DStream that represents a continuous stream of data as a series of RDDs. Transformations applied to DStreams are similarly applied to the underlying RDDs. Spark Streaming also supports window operations, output operations, and integrating streaming with Spark's ML and graph processing capabilities.
Fisl15 Streaming de video ao vivo na globo.comLeandro Moreira
油
O documento resume 10 li巽探es aprendidas ao transmitir v鱈deos ao vivo na globo.com. A principal li巽達o 辿 que o protocolo HLS 辿 muito melhor do que o RTMP para transmiss達o de v鱈deo, resultando em menos falhas de reprodu巽達o, melhor qualidade de v鱈deo e mais tempo assistido. Outra li巽達o 辿 a import但ncia crucial de medi巽達o e m辿tricas para otimizar o desempenho. Por fim, o documento defende iniciativas de c坦digo aberto para tornar o software mais gen辿rico e atrair contribui巽探es da comunidade.
This material is made to educate operators, who deal with cassandra in production environment and based on cassandra version 1.1.X
襭 Cassandra襯 蟆曙 伎蠍 , 伎襯 蟲 襭襦 Cassandra 1.1.X襯 蠍一朱 る 襭.
This document provides an overview of NoSQL databases. It begins by defining NoSQL as non-relational databases that are distributed, open source, and horizontally scalable. It then discusses some of the limitations of relational databases that led to the rise of NoSQL, such as issues with scalability and the need for flexible schemas. The document also summarizes some key NoSQL concepts, including the CAP theorem, ACID versus BASE, and eventual consistency. It provides examples of use cases for NoSQL databases and discusses some common NoSQL database types and how they address scalability.
The document provides a summary of the history and current state of big data and Hadoop. It discusses how the concept of big data emerged in the late 1990s and was further popularized by McKinsey in 2011. It then outlines the evolution of definitions and technologies around big data, including the development of Hadoop from the early 2000s onward. The document also analyzes current adoption trends, use cases, and market forecasts for big data and Hadoop.
This document summarizes several Cassandra anti-patterns including:
- Using a non-Oracle JVM which is not recommended.
- Putting the commit log and data directories on the same disk which can impact performance.
- Using EBS volumes on EC2 which can have unpredictable performance and throughput issues.
- Configuring overly large JVM heaps over 16GB which can cause garbage collection issues.
- Performing large batch mutations in a single operation which risks timeouts if not broken into smaller batches.
This document provides an introduction and overview of Couchbase Server, a NoSQL document database. It describes Couchbase Server as the leading open source project focused on distributed database technology. It outlines key features such as easy scalability, always-on availability, flexible data modeling using JSON documents, and core features including clustering, replication, indexing and querying. The document also provides examples of basic write, read and update operations on a single node and cluster, adding nodes, handling node failures, indexing and querying capabilities, and cross data center replication.
Cassandra is a distributed, decentralized, wide column store NoSQL database modeled after Amazon's Dynamo and Google's Bigtable. It provides high availability with no single point of failure, elastic scalability and tunable consistency. Cassandra uses consistent hashing to partition and distribute data across nodes, vector clocks to track data versions for consistency, and Merkle trees to detect and repair inconsistencies between replicas.
Cassandra nice use cases and worst anti patternsDuyhai Doan
油
This document discusses Cassandra use cases and anti-patterns. Some good use cases include rate limiting, fraud prevention, account validation, and storing sensor time series data. Poor designs include using Cassandra like a queue, storing null values, intensive updates to the same column, and dynamically changing the schema. The document provides examples and explanations of how to properly implement these scenarios in Cassandra.
strangeloop 2012 apache cassandra anti patternsMatthew Dennis
油
The document outlines various anti-patterns in using Apache Cassandra, emphasizing that it performs best on commodity hardware without SAN and detailing issues with oversized JVM heaps, incorrect file handle limits, and misconfigured caches. It highlights the importance of proper configuration, such as using initial tokens, avoiding large rows in caches, and not using certain features like BOP (batching of operations) improperly. Lastly, it suggests using OSS tools like OPS and avoiding non-standard JVMs for stability and compatibility.
This document discusses managing Apache Cassandra at scale. It provides an overview of Cassandra's history and evolution from Dynamo and BigTable. It also discusses Cassandra's data model and how it handles operations like reads, writes and updates in a distributed system without relying on read-modify-writes. The document also covers Cassandra best practices like using collections, lightweight transactions and time series data modeling to optimize for scalability.
Lessons Learned on Java Tuning for Our Cassandra Clusters (Carlos Monroy, Kne...DataStax
油
The document outlines Knewton's experience with JVM tuning for Cassandra clusters, highlighting challenges and successful strategies implemented to improve database performance. Key initiatives included updating memtable allocation settings, changing to garbage first garbage collection (G1GC), and enhancing monitoring tools. The findings emphasize the importance of testing configuration changes in collaboration with the development team to optimize performance and reduce technical debt.
Spark Streaming allows for scalable, high-throughput, fault-tolerant stream processing of live data streams. It works by chopping data streams into batches, treating each batch as an RDD, and processing them using RDD transformations and operations. This provides a simple abstraction called a DStream that represents a continuous stream of data as a series of RDDs. Transformations applied to DStreams are similarly applied to the underlying RDDs. Spark Streaming also supports window operations, output operations, and integrating streaming with Spark's ML and graph processing capabilities.
2025 蟾讌 一危 レ 襷 危エ覲願, 企殊磯 一危磯ゼ 朱 る蠍 觜るれ 螳 | Explore the prospects for the data market by 2025 and introduce services to efficiently address data in the cloud
The document discusses Arquillian, a testing framework that allows integration tests to be written and run similarly to unit tests. It can package components and their dependencies into a deployable archive. Tests using Arquillian can be run inside an IDE, incrementally built, and debugged like unit tests. The framework provides a component model for tests that encapsulates business logic and allows flexible configuration of the test classpath and deployment.