Horizon is a distributed SQL database that allows users to query and analyze big data stored in HBase using a familiar SQL interface. It uses the H2 database engine and customizes HBase's data model to provide features like indexing, partitioning, and SQL support. Horizon aims to make big data more accessible while maintaining HBase's scalability. It will integrate with Hadoop ecosystems and provide high performance data loading, scanning, and analysis tools. Horizon's architecture distributes the SQL engine across servers and uses HBase as the distributed storage layer.
- The document discusses the vision for a new big data database (BigDataBase) with high scalability and the ability to store and analyze petabytes of data in real-time.
- An initial trial using HBase as the storage engine for a customized SQL interface showed potential but had limitations in features, models, and performance.
- The document proposes wrapping HBase in a middleware to add it as a pluggable storage engine to MySQL/PostgreSQL, enabling SQL queries over HBase's distributed data storage.
- It also considers designing a new SQL server from scratch that interfaces with HBase through the middleware, implementing additional database features like indexing, ACID compliance, and partitioning for big data work
E-learning refers to electronically-supported learning and involves the delivery of educational content via the internet, intranets, audio or video tape, satellite broadcast, interactive TV, and CD-ROM. It encompasses a wide variety of applications and processes including web-based learning, computer-based learning, virtual classrooms, and digital collaboration. E-learning is used by organizations for reasons such as saving money, reaching geographically dispersed groups, and ensuring consistency. Effective e-learning requires sound instructional design and the use of technologies like learning management systems to deliver content and track learner progress. Emerging technologies being used to deliver e-learning include podcasting, vodcasting, wikis, and blogs.
Hanborq optimizations on hadoop map reduce 20120221aSchubert Zhang
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Hanborq has developed optimizations to improve the performance of Hadoop MapReduce in three key areas:
1. The runtime environment uses a worker pool and improved scheduling to reduce job completion times from tens of seconds to near real-time.
2. The processing engine utilizes techniques like sendfile for zero-copy data transfer and Netty batch fetching to reduce network overhead and CPU usage during shuffling.
3. Sort avoidance algorithms are implemented to minimize expensive sorting operations through techniques such as early reduce and hash aggregation.
The document evaluates the performance of HBase version 0.20.0 on a small cluster. It describes the testbed setup including hardware specifications and Hadoop/HBase configuration parameters. A series of experiments are run to test random reads, random writes, sequential reads, sequential writes, and scans. The results show significant performance improvements over previous versions, getting closer to the performance levels of Google BigTable as reported in their paper.
The document provides an evaluation report of DaStor, a Cassandra-based data storage and query system. It summarizes the testbed hardware configuration including 9 nodes with 112 cores and 144GB RAM. It also describes the DaStor configuration, data schema for call detail records (CDR), storage architecture with indexing scheme, and benchmark results showing a throughput of around 80,000 write operations per second for the cluster.
The document summarizes and compares several distributed file systems, including Google File System (GFS), Kosmos File System (KFS), Hadoop Distributed File System (HDFS), GlusterFS, and Red Hat Global File System (GFS). GFS, KFS and HDFS are based on the GFS architecture of a single metadata server and multiple chunkservers. GlusterFS uses a decentralized architecture without a metadata server. Red Hat GFS requires a SAN for high performance and scalability. Each system has advantages and limitations for different use cases.
Horizon is a distributed SQL database that allows users to query and analyze big data stored in HBase using a familiar SQL interface. It uses the H2 database engine and customizes HBase's data model to provide features like indexing, partitioning, and SQL support. Horizon aims to make big data more accessible while maintaining HBase's scalability. It will integrate with Hadoop ecosystems and provide high performance data loading, scanning, and analysis tools. Horizon's architecture distributes the SQL engine across servers and uses HBase as the distributed storage layer.
- The document discusses the vision for a new big data database (BigDataBase) with high scalability and the ability to store and analyze petabytes of data in real-time.
- An initial trial using HBase as the storage engine for a customized SQL interface showed potential but had limitations in features, models, and performance.
- The document proposes wrapping HBase in a middleware to add it as a pluggable storage engine to MySQL/PostgreSQL, enabling SQL queries over HBase's distributed data storage.
- It also considers designing a new SQL server from scratch that interfaces with HBase through the middleware, implementing additional database features like indexing, ACID compliance, and partitioning for big data work
E-learning refers to electronically-supported learning and involves the delivery of educational content via the internet, intranets, audio or video tape, satellite broadcast, interactive TV, and CD-ROM. It encompasses a wide variety of applications and processes including web-based learning, computer-based learning, virtual classrooms, and digital collaboration. E-learning is used by organizations for reasons such as saving money, reaching geographically dispersed groups, and ensuring consistency. Effective e-learning requires sound instructional design and the use of technologies like learning management systems to deliver content and track learner progress. Emerging technologies being used to deliver e-learning include podcasting, vodcasting, wikis, and blogs.
Hanborq optimizations on hadoop map reduce 20120221aSchubert Zhang
?
Hanborq has developed optimizations to improve the performance of Hadoop MapReduce in three key areas:
1. The runtime environment uses a worker pool and improved scheduling to reduce job completion times from tens of seconds to near real-time.
2. The processing engine utilizes techniques like sendfile for zero-copy data transfer and Netty batch fetching to reduce network overhead and CPU usage during shuffling.
3. Sort avoidance algorithms are implemented to minimize expensive sorting operations through techniques such as early reduce and hash aggregation.
The document evaluates the performance of HBase version 0.20.0 on a small cluster. It describes the testbed setup including hardware specifications and Hadoop/HBase configuration parameters. A series of experiments are run to test random reads, random writes, sequential reads, sequential writes, and scans. The results show significant performance improvements over previous versions, getting closer to the performance levels of Google BigTable as reported in their paper.
The document provides an evaluation report of DaStor, a Cassandra-based data storage and query system. It summarizes the testbed hardware configuration including 9 nodes with 112 cores and 144GB RAM. It also describes the DaStor configuration, data schema for call detail records (CDR), storage architecture with indexing scheme, and benchmark results showing a throughput of around 80,000 write operations per second for the cluster.
The document summarizes and compares several distributed file systems, including Google File System (GFS), Kosmos File System (KFS), Hadoop Distributed File System (HDFS), GlusterFS, and Red Hat Global File System (GFS). GFS, KFS and HDFS are based on the GFS architecture of a single metadata server and multiple chunkservers. GlusterFS uses a decentralized architecture without a metadata server. Red Hat GFS requires a SAN for high performance and scalability. Each system has advantages and limitations for different use cases.