Hive et HadoopDB
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Efficient Processing of Data Warehousing Queries in a Split
2 1 Hive and Hadoop Hive [4] is an open-source data warehousing infrastructure built on top of Hadoop [2] Hive accepts queries expressed in a SQL-like language called HiveQL and executes them against data stored in the Hadoop Distributed File System (HDFS) A big limitation of the current implementation of Hive is its data storage layer |
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HadoopDB in Action: Building Real World Applications
tends Hive [9] to provide a SQL interface to HadoopDB See our previous work [5] for more details on the HadoopDB architecture HadoopDB supports any JDBC-compliant database server as an underlying DBMS layer giving application designers a lot of exibility to choose the most e cient database tech-nology for a given application In the original |
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HadoopDB: An architectural hybrid of MapReduce and DBMS
Best price/performance → data partitioned across 100-1000s of cheap commodity shared-nothing machines Clouds of processing nodes on demand pay for what you use Major Trends Data explosion: Automation of business processes proliferation of digital devices eBay has a 6 5 petabyte warehouse 2 |
Does Hadoop outperform hive?
Hadoop (with and without Hive) performs a brute-force, complete scan of all data in a file. The other systems, however, benefit from us- ing clustered indices on the pageRank column. Hence, in general HadoopDB and the parallel DBMSs are able to outperform Hadoop.
What is MapReduce in Hadoop?
The basic idea behind HadoopDB is to use MapReduce as the communication layer above multiple nodes running single-node DBMS instances. Queries are expressed in SQL, translated into MapReduce by extending existing tools, and as much work as possible is pushed into the higher performing sin- gle node databases.
What is Hadoop DB?
HadoopDB: an architectural hybrid of MapReduce and DBMS technologies for analytical workloads Computer systems organization Architectures Distributed architectures Information systems Data management systems Database management system engines Parallel and distributed DBMSs Information systems applications Recommendations
What are the two layers of Hadoop?
Hadoop consits of two layers: (i) a data storage layer or the Hadoop Dis- tributed File System (HDFS) and (ii) a data processing layer or the MapReduce Framework. HDFS is a block-structured file system managed by a central NameNode. Individual files are broken into blocks of a fixed size and distributed across multiple DataNodes in the cluster.
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Gestion et exploration des grandes masses de données
22 janv. 2015 Colloque Mastodons CNRS |
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HaoLap: a Hadoop based OLAP system for big data
21 mai 2015 HaoLap and compares that with Hive HadoopDB |
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HadoopDB in Action: Building Real World Applications
HadoopDB is a hybrid of MapReduce and DBMS technolo- gies designed to meet the growing demand of tends Hive [9] to provide a SQL interface to HadoopDB. |
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CNRS
23 janv. 2014 Hive vs HadoopDB. – Hive > HadoopDB. ? Sélection avec index. ? Group by sur requêtes non sélectives (Hadoop ne profite pas des. |
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HadoopDB: An Architectural Hybrid of MapReduce and DBMS
Hence we use PostgreSQL as the database layer and Hadoop as the communication layer |
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HadoopDB: An Architectural Hybrid of MapReduce and DBMS
HadoopDB provides a parallel database front-end to data analysts enabling them to process SQL queries. The SMS planner extends Hive [11]. |
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Efficient Processing of Data Warehousing Queries in a Split
16 juin 2011 featuring a SQL interface (Hive). We show that HadoopDB successfully competes with other systems. Categories and Subject Descriptors. |
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Integration of Large-Scale Data Processing Systems and Traditional
Across a variety of data process- ing tasks HadoopDB outperformed simple SQL-into-. MapReduce translation layers (such as Hive) |
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Integration of Large-Scale Data Processing Systems and Traditional
Across a variety of data process- ing tasks HadoopDB outperformed simple SQL-into-. MapReduce translation layers (such as Hive) |
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HaoLap : A Hadoop based OLAP system for big data
multidimensional data model. Some practical data warehouses based on Hadoop have emerged such as Hive HadoopDB and HBase. Hive |
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HadoopDB: An Architectural Hybrid of MapReduce and - Cs Umd
HadoopDB provides a parallel database front-end to data analysts enabling them to process SQL queries The SMS planner extends Hive [11] Hive transforms |
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Gestion et exploration des grandes masses de données - CNRS
22 jan 2015 · 22/1/15 Emmanuel Gangler – Workshop Mastodons 8/16 Quelques résultats (3) Focus Expérimentation sous Hive et HadoopDB : Synthèse |
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HadoopDB in Action - Computer Science - Yale University
HadoopDB is a hybrid of MapReduce and DBMS technolo- gies, designed to meet tends Hive [9] to provide a SQL interface to HadoopDB See our previous |
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HadoopDB: An Architectural Hybrid of MapReduce and DBMS
There is a map and a reduce phase in these queries HadoopDB pushes the SQL operators' execution in to the PostGreSQL Using Hive's query optimizer |
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DGFIndex for Smart Grid: Enhancing Hive with a Cost-Effective
is 2-63 times faster than existing indexes in Hive, 2-94 times faster than HadoopDB, 2-75 times faster than scanning the whole table in different query selectivity |
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SQLMR : A Scalable Database Management - ResearchGate
results demonstrate both performance and scalability advantage of SQLMR compared to MySQL and two NoSQL data processing systems, Hive and HadoopDB |
