Tar -xvzf hadoop-2.7.3.tar.gz tar -xvzf apache-hive-1.2.1-bin.tar.gz Further amend the bashrc file. You'll need to identify the path to where the archives were unpacked. Software Hadoop Version. 2.7.1 Download link(s) Use the provided command in the tutorial File size 210 MB Install size Variable. $ sudo tar vxzf hadoop-2.7.1.tar.gz -C /usr/local Now, move to the folder of Hadoop and setup the ownership and permissoins. $ cd /usr/local. 编译Hadoop 第一步：解压hadoop源码包 tar -zxvf /root/hadoop-2.7.7-src.tar.gz -C /root/apps/ 第二步：在编译之前防止 java.lang.OutOfMemoryError: Java heap space 堆栈问题,在centos系统中执 行命令 export MAVENOPTS='-Xms256m -Xmx512m' 第三步：切换工作目录到hadoop-2.7.7根目录下： cd /root/apps/hadoop-2. Reference:: The following instructions are applicable to Ubuntu 14.04, Ubuntu 15.04 & Ubuntu 15.10 (All 64 & 32 bits). Steps:: PDF version of this tutorial: Hadoop Installation Steps # Ope.
The SnappyData team continues to march towards a 1.0GA and we are pleased to announce the availability of version 0.9 of the platform today. This release contains significant new functionality, several performance enhancements, design changes to make the platform scale better and a new and improved console that improves enterprise readiness of the SnappyData cluster.
In-memory but disk persistent, by default now
Until this release, by default, all tables were only memory resident and required explicit configuration for disk persistence (e.g. using the 'persistent' clause in 'create table'). From this release, we make all tables all persist to disk, by default. You can explicitly turn this OFF for pure memory-only tables.
- Improved “Unified Memory Manager” with more accurate accounting of memory. The previous release could prematurely spill to disk or cause GC pauses (SNAP-1235).
- Support for Off-Heap storage in column store (SNAP-1454). The previous release required users to over allocate Java heap memory to avoid GC pauses or exposed applications to an increased risk of stop-the-world GC pauses. In addition to performance benefits, off-heap storage contributes to predictable system performance and behavior and is absolutely recommended for all production deployments.
Version 0.9 includes several product enhancements that contribute to improved product performance.
- The disk storage design for Column tables is more optimized. Before, the logical disk storage unit was still a set of rows. Instead now, the unit is now a set of column values making queries that require faulting data from disk significantly more efficient (SNAP-990).
- The Query engine now caches the physical plan as well as the generated code for queries. Spark, likewise SnappyData, dynamically generates JVM byte code for the query, compiles and caches this generated code so any subsequent execution of the same query is much faster. But, often queries are similar not the same. For instance, the bound constants change( common in Where clauses). This meant the compiled plans are all that useful. Now, the generated code tokenizes literals and constants so that subsequent similar queries with different bound values execute much faster. (SNAP-1346).
- Previous to 0.9, SnappyData was not optimized for PreparedStatements (JDBC) when the query was routed to the Spark Catalyst engine. Now, it is. (SNAP-1323).
- SnappyData offers a smart connector mode, which allows Spark applications running in a remote cluster to intelligently and efficiently (very high degree of parallelism) access data stored in a SnappyData cluster. Version 0.9 offers a redesigned smart connector which acts as a client to the SnappyData cluster, offering much high levels of scaling for both the client and also improves the ability of the cluster to handle such connections without impacting the cluster’s ability to scale (Previous versions of the connector had to join the cluster as a peer member limiting the scalability of the cluster) (SNAP-1286)
- Consistency improvements: This release introduces snapshot Isolation semantics, by default, while processing queries using an MVCC algorithm so queries are guaranteed to access a stable view of the database (SNAP-1304).
- Pulse Console: SnappyData Pulse has been redesigned to provide both developers and operations personnel with useful insights into the running of the SnappyData cluster. Improvements include
-- Redesigned member view which displays detailed member description, heap and off-heap usage along with snappy storage and execution splits
-- Cluster level aggregate memory and CPU usage
-- SQL tab that shows the SQL statements executed within the system with the ability to view query plans for the same
Select bug fixes and performance related fixes:
- Starting version 0.9, row tables support the Boolean data type
- Support for slash ('/') and special characters in column names (SNAP-1705).
- Scans and ingest through code generation could fail if the generated code of a single method exceeds 64k (SNAP-1384).
For the complete list of tickets that were fixed in this release, see ReleaseNotes.txt.
Description of download artifacts:
Tar.gz Extract Linux
|snappydata-0.9-bin.tar.gz||Full product binary (includes Hadoop 2.7)|
|snappydata-0.9-bin.zip||Full product binary (includes Hadoop 2.7)|
|snappydata-0.9-without-hadoop-bin.tar.gz||Product without the Hadoop dependency JARs|
|snappydata-0.9-without-hadoop-bin.zip||Product without the Hadoop dependency JARs|
|snappydata-client-1.5.5.jar||Client (JDBC) JAR|
|snappydata-core_2.11-0.9.jar||Only dependency to connect to SnappyStore from Apache Spark 2.0.X cluster (Smart Connector mode)|
|snappydata-0.9-odbc32.zip||32-bit ODBC driver for 32-bit Windows. Extract and run the msi.|
|snappydata-0.9-odbc64.zip||64-bit ODBC driver for 64-bit Windows. Extract and run the msi.|
|snappydata-0.9-odbc32_64.zip||32-bit ODBC driver for 64-bit Windows. Extract and run the msi.|
|ODBC-and-Tableau-Setup.pdf||Installation instructions for the ODBC driver including Tableau setup.|
|odbc-snappydata.tdc||TDC file for Tableau setup (see setup guide for details)|
|snappydata-zeppelin-0.7.1.jar||The Zeppelin interpreter jar for SnappyData, compatible with Apache Zeppelin 0.7|