Hypertable is an open source project based on published best practices and our own experience in solving large-scale data-intensive tasks. Our goal is nothing. Modeled after Bigtable. ➢ Implemented in C++. ➢ Project Started in March ➢ Runs on top of HDFS. ➢ Thrift Interface for all popular languages. ○ Java. hypertable> create namespace “Tutorial”;. hypertable> use Tutorial;. create table. hypertable> CREATE TABLE QueryLogByUserID (Query.
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Hypertable is a high performance, open source, massively scalable database modeled after Bigtable, Google’s proprietary, massively scalable database. This page provides a brief overview of Hypertable, comparing it with a relational database, highlighting some of its unique features, and illustrating how it scales.
The following is a list of some of the main differences. Tables in Hypertable can be thought of as massive tables of data, sorted by a single primary key, the row key.
A relational database assumes that each column defined tutoriap the table schema will have a value for each row that is present in the table. NULL values are usually represented with a special marker e.
The primary key and column identifier are implicitly associated with each cell based on its physical position within the layout.
The following diagram illustrates how a relational database table might be laid out on disk. Hypertable extends the traditional two-dimensional table model by adding a third dimension: This timestamp dimension can be thought of as representing different versions of each table cell, as illustrated in the following diagram.
When queried, the most recent cell version is returned first. The timestamp can be supplied by the application tutotial insert time, or can be auto-generated default. This feature provides a way for users to introduce sparse column data that can be easily selected with Hypertable Query Language HQL or any of the other query interfaces.
This section illustrates how Hypertable scales. Let’s say the system has been loaded with the following two tables, a session ID table and a crawl database table.
Over time, Hypertable will break these tables into ranges and distribute them to what are known as RangeServer processes. These processes manage ranges of table data and run on all slave server machines in the cluster. For example, assuming there are three slave servers, the following diagram shows what the system might look like over time. As can be seen by the diagram, the three servers are filled to capacity.
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Adding more capacity is a simple matter of adding new commodity class servers and starting RangeServer processes on the new machines. Hypertable will detect that there are new servers available with plenty of spare capacity and will automatically migrate ranges from the overloaded machines onto the new ones.
This tutoria, migration process has the effect of balancing load across the entire cluster and opening up additional capacity.