This paper research Hive performance optimization mainly from the two aspects of MapReduce scheduling and Hive performance tuning.MapReduce's programming model and its implementation process is analyzed,and parameters are tuned from the map side and reduce side.Then Hive's framework is researched from the aspects of the partition table,the external surface and common data file compression,the line storage and column type storage.The experimental results show that snappy compression and orcfile/parquet storage format can improve the efficiency of query for the column type queries, and has good compatibility
The data warehouse system Hive has emerged as an important facility for supporting data computing an...
MapReduce framework has become the state-of-the-art paradigm for large-scale data processing. In our...
Hive is a tool that allows the implementation of Data Warehouses for Big Data contexts, organizing d...
Hive table is one of the big data tables which relies on structural data. By default, it stores the ...
ABSTRACT Apache Hive is a widely used data warehouse system for Apache Hadoop, and has been adopted ...
Abstract-—As a core component of Hadoop that is a cloud open platform, MapReduce is a distributed an...
In this work, we present a set of techniques that considerably improve the performance of executing ...
As the era of “big data” has arrived, more and more companies start using distributed file systems t...
The performance of the MapReduce-based Cloud data warehouses mainly depends on the virtual hardware ...
Apache Hadoop is an open source framework that deals with the distributed computing of large dataset...
In this work, we present a set of techniques that considerably improve the performance of executing ...
MapReduce is a popular model of executing time-consuming analytical queries as a batch of tasks on l...
Apache Hadoop has provided solutions to the obstacles related to the Big Data processing. Hadoop sto...
The size of data coming from various has increased rapidly. Within few seconds; terabytes of data is...
Executing expensive queries over many large tables can be prohibitively time consuming in convention...
The data warehouse system Hive has emerged as an important facility for supporting data computing an...
MapReduce framework has become the state-of-the-art paradigm for large-scale data processing. In our...
Hive is a tool that allows the implementation of Data Warehouses for Big Data contexts, organizing d...
Hive table is one of the big data tables which relies on structural data. By default, it stores the ...
ABSTRACT Apache Hive is a widely used data warehouse system for Apache Hadoop, and has been adopted ...
Abstract-—As a core component of Hadoop that is a cloud open platform, MapReduce is a distributed an...
In this work, we present a set of techniques that considerably improve the performance of executing ...
As the era of “big data” has arrived, more and more companies start using distributed file systems t...
The performance of the MapReduce-based Cloud data warehouses mainly depends on the virtual hardware ...
Apache Hadoop is an open source framework that deals with the distributed computing of large dataset...
In this work, we present a set of techniques that considerably improve the performance of executing ...
MapReduce is a popular model of executing time-consuming analytical queries as a batch of tasks on l...
Apache Hadoop has provided solutions to the obstacles related to the Big Data processing. Hadoop sto...
The size of data coming from various has increased rapidly. Within few seconds; terabytes of data is...
Executing expensive queries over many large tables can be prohibitively time consuming in convention...
The data warehouse system Hive has emerged as an important facility for supporting data computing an...
MapReduce framework has become the state-of-the-art paradigm for large-scale data processing. In our...
Hive is a tool that allows the implementation of Data Warehouses for Big Data contexts, organizing d...