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From yo...@apache.org
Subject [07/57] [abbrv] [partial] incubator-hawq-docs git commit: HAWQ-1254 Fix/remove book branching on incubator-hawq-docs
Date Tue, 10 Jan 2017 23:53:58 GMT
http://git-wip-us.apache.org/repos/asf/incubator-hawq-docs/blob/de1e2e07/mdimages/svg/hawq_resource_queues.svg
----------------------------------------------------------------------
diff --git a/mdimages/svg/hawq_resource_queues.svg b/mdimages/svg/hawq_resource_queues.svg
deleted file mode 100644
index 4fdf655..0000000
--- a/mdimages/svg/hawq_resource_queues.svg
+++ /dev/null
@@ -1,340 +0,0 @@
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-title: Elastic Query Execution Runtime
----
-
-HAWQ uses dynamically allocated virtual segments to provide resources for query execution.
-
-In HAWQ 1.x, the number of segments \(compute resource carrier\) used to run a query is fixed, no matter whether the underlying query is big query requiring many resources or a small query requiring little resources. This architecture is simple, however it uses resources inefficiently.
-
-To address this issue, HAWQ now uses the elastic query execution runtime feature, which is based on virtual segments. HAWQ allocates virtual segments on demand based on the costs of queries. In other words, for big queries, HAWQ starts a large number of virtual segments, while for small queries HAWQ starts fewer virtual segments.
-
-## Storage
-
-In HAWQ, the number of invoked segments varies based on cost of query. In order to simplify table data management, all data of one relation are saved under one HDFS folder.
-
-For all the HAWQ table storage formats, AO \(Append-Only\) and Parquet, the data files are splittable, so that HAWQ can assign multiple virtual segments to consume one data file concurrently to increase the parallelism of a query.
-
-## Physical Segments and Virtual Segments
-
-In HAWQ, only one physical segment needs to be installed on one host, in which multiple virtual segments can be started to run queries. HAWQ allocates multiple virtual segments distributed across different hosts on demand to run one query. Virtual segments are carriers \(containers\) for resources such as memory and CPU. Queries are executed by query executors in virtual segments.
-
-**Note:** In this documentation, when we refer to segment by itself, we mean a *physical segment*.
-
-## Virtual Segment Allocation Policy
-
-Different number of virtual segments are allocated based on virtual segment allocation policies. The following factors determine the number of virtual segments that are used for a query:
-
--   Resources available at the query running time
--   The cost of the query
--   The distribution of the table; in other words, randomly distributed tables and hash distributed tables
--   Whether the query involves UDFs and external tables
--   Specific server configuration parameters, such as `default_hash_table_bucket_number` for hash table queries and `hawq_rm_nvseg_perquery_limit`

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-title: HAWQ Architecture
----
-
-This topic presents HAWQ architecture and its main components.
-
-In a typical HAWQ deployment, each slave node has one physical HAWQ segment, an HDFS DataNode and a NodeManager installed. Masters for HAWQ, HDFS and YARN are hosted on separate nodes.
-
-The following diagram provides a high-level architectural view of a typical HAWQ deployment.
-
-![](../mdimages/hawq_high_level_architecture.png)
-
-HAWQ is tightly integrated with YARN, the Hadoop resource management framework, for query resource management. HAWQ caches containers from YARN in a resource pool and then manages those resources locally by leveraging HAWQ's own finer-grained resource management for users and groups. To execute a query, HAWQ allocates a set of virtual segments according to the cost of a query, resource queue definitions, data locality and the current resource usage in the system. Then the query is dispatched to corresponding physical hosts, which can be a subset of nodes or the whole cluster. The HAWQ resource enforcer on each node monitors and controls the real time resources used by the query to avoid resource usage violations.
-
-The following diagram provides another view of the software components that constitute HAWQ.
-
-![](../mdimages/hawq_architecture_components.png)
-
-## <a id="hawqmaster"></a>HAWQ Master 
-
-The HAWQ *master* is the entry point to the system. It is the database process that accepts client connections and processes the SQL commands issued. The HAWQ master parses queries, optimizes queries, dispatches queries to segments and coordinates the query execution.
-
-End-users interact with HAWQ through the master and can connect to the database using client programs such as psql or application programming interfaces \(APIs\) such as JDBC or ODBC.
-
-The master is where the `global system catalog` resides. The global system catalog is the set of system tables that contain metadata about the HAWQ system itself. The master does not contain any user data; data resides only on *HDFS*. The master authenticates client connections, processes incoming SQL commands, distributes workload among segments, coordinates the results returned by each segment, and presents the final results to the client program.
-
-## <a id="hawqsegment"></a>HAWQ Segment 
-
-In HAWQ, the *segments* are the units that process data simultaneously.
-
-There is only one *physical segment* on each host. Each segment can start many Query Executors \(QEs\) for each query slice. This makes a single segment act like multiple virtual segments, which enables HAWQ to better utilize all available resources.
-
-**Note:** In this documentation, when we refer to segment by itself, we mean a *physical segment*.
-
-A *virtual segment* behaves like a container for QEs. Each virtual segment has one QE for each slice of a query. The number of virtual segments used determines the degree of parallelism \(DOP\) of a query.
-
-A segment differs from a master because it:
-
--   Is stateless.
--   Does not store the metadata for each database and table.
--   Does not store data on the local file system.
-
-The master dispatches the SQL request to the segments along with the related metadata information to process. The metadata contains the HDFS url for the required table. The segment accesses the corresponding data using this URL.
-
-## <a id="hawqinterconnect"></a>HAWQ Interconnect 
-
-The *interconnect* is the networking layer of HAWQ. When a user connects to a database and issues a query, processes are created on each segment to handle the query. The *interconnect* refers to the inter-process communication between the segments, as well as the network infrastructure on which this communication relies. The interconnect uses standard Ethernet switching fabric.
-
-By default, the interconnect uses UDP \(User Datagram Protocol\) to send messages over the network. The HAWQ software performs the additional packet verification beyond what is provided by UDP. This means the reliability is equivalent to Transmission Control Protocol \(TCP\), and the performance and scalability exceeds that of TCP. If the interconnect used TCP, HAWQ would have a scalability limit of 1000 segment instances. With UDP as the current default protocol for the interconnect, this limit is not applicable.
-
-## <a id="topic_jjf_11m_g5"></a>HAWQ Resource Manager 
-
-The HAWQ resource manager obtains resources from YARN and responds to resource requests. Resources are buffered by the HAWQ resource manager to support low latency queries. The HAWQ resource manager can also run in standalone mode. In these deployments, HAWQ manages resources by itself without YARN.
-
-See [How HAWQ Manages Resources](../resourcemgmt/HAWQResourceManagement.html) for more details on HAWQ resource management.
-
-## <a id="topic_mrl_psq_f5"></a>HAWQ Catalog Service 
-
-The HAWQ catalog service stores all metadata, such as UDF/UDT information, relation information, security information and data file locations.
-
-## <a id="topic_dcs_rjm_g5"></a>HAWQ Fault Tolerance Service 
-
-The HAWQ fault tolerance service \(FTS\) is responsible for detecting segment failures and accepting heartbeats from segments.
-
-See [Understanding the Fault Tolerance Service](../admin/FaultTolerance.html) for more information on this service.
-
-## <a id="topic_jtc_nkm_g5"></a>HAWQ Dispatcher 
-
-The HAWQ dispatcher dispatches query plans to a selected subset of segments and coordinates the execution of the query. The dispatcher and the HAWQ resource manager are the main components responsible for the dynamic scheduling of queries and the resources required to execute them.

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-title: What is HAWQ?
----
-
-HAWQ is a Hadoop native SQL query engine that combines the key technological advantages of MPP database with the scalability and convenience of Hadoop. HAWQ reads data from and writes data to HDFS natively.
-
-HAWQ delivers industry-leading performance and linear scalability. It provides users the tools to confidently and successfully interact with petabyte range data sets. HAWQ provides users with a complete, standards compliant SQL interface. More specifically, HAWQ has the following features:
-
--   On-premise or cloud deployment
--   Robust ANSI SQL compliance: SQL-92, SQL-99, SQL-2003, OLAP extension
--   Extremely high performance- many times faster than other Hadoop SQL engines
--   World-class parallel optimizer
--   Full transaction capability and consistency guarantee: ACID
--   Dynamic data flow engine through high speed UDP based interconnect
--   Elastic execution engine based on on-demand virtual segments and data locality
--   Support multiple level partitioning and List/Range based partitioned tables.
--   Multiple compression method support: snappy, gzip
--   Multi-language user defined function support: Python, Perl, Java, C/C++, R
--   Advanced machine learning and data mining functionalities through MADLib
--   Dynamic node expansion: in seconds
--   Most advanced three level resource management: Integrate with YARN and hierarchical resource queues.
--   Easy access of all HDFS data and external system data \(for example, HBase\)
--   Hadoop Native: from storage \(HDFS\), resource management \(YARN\) to deployment \(Ambari\).
--   Authentication & granular authorization: Kerberos, SSL and role based access
--   Advanced C/C++ access library to HDFS and YARN: libhdfs3 and libYARN
--   Support for most third party tools: Tableau, SAS et al.
--   Standard connectivity: JDBC/ODBC
-
-HAWQ breaks complex queries into small tasks and distributes them to MPP query processing units for execution.
-
-HAWQ's basic unit of parallelism is the segment instance. Multiple segment instances on commodity servers work together to form a single parallel query processing system. A query submitted to HAWQ is optimized, broken into smaller components, and dispatched to segments that work together to deliver a single result set. All relational operations - such as table scans, joins, aggregations, and sorts - simultaneously execute in parallel across the segments. Data from upstream components in the dynamic pipeline are transmitted to downstream components through the scalable User Datagram Protocol \(UDP\) interconnect.
-
-Based on Hadoop's distributed storage, HAWQ has no single point of failure and supports fully-automatic online recovery. System states are continuously monitored, therefore if a segment fails, it is automatically removed from the cluster. During this process, the system continues serving customer queries, and the segments can be added back to the system when necessary.
-
-These topics provide more information about HAWQ and its main components:
-
-* <a class="subnav" href="./HAWQArchitecture.html">HAWQ Architecture</a>
-* <a class="subnav" href="./TableDistributionStorage.html">Table Distribution and Storage</a>
-* <a class="subnav" href="./ElasticSegments.html">Elastic Segments</a>
-* <a class="subnav" href="./ResourceManagement.html">Resource Management</a>
-* <a class="subnav" href="./HDFSCatalogCache.html">HDFS Catalog Cache</a>
-* <a class="subnav" href="./ManagementTools.html">Management Tools</a>
-* <a class="subnav" href="./RedundancyFailover.html">High Availability, Redundancy, and Fault Tolerance</a>

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-title: HDFS Catalog Cache
----
-
-HDFS catalog cache is a caching service used by HAWQ master to determine the distribution information of table data on HDFS.
-
-HDFS is slow at RPC handling, especially when the number of concurrent requests is high. In order to decide which segments handle which part of data, HAWQ needs data location information from HDFS NameNodes. HDFS catalog cache is used to cache the data location information and accelerate HDFS RPCs.

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-title: HAWQ Management Tools
----
-
-HAWQ management tools are consolidated into one `hawq` command.
-
-The `hawq` command can init, start and stop each segment separately, and supports dynamic expansion of the cluster.
-
-See [HAWQ Management Tools Reference](../reference/cli/management_tools.html) for a list of all tools available in HAWQ.

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-title: High Availability, Redundancy and Fault Tolerance
----
-
-HAWQ ensures high availability for its clusters through system redundancy. HAWQ deployments utilize platform hardware redundancy, such as RAID for the master catalog, JBOD for segments and network redundancy for its interconnect layer. On the software level, HAWQ provides redundancy via master mirroring and dual cluster maintenance. In addition, HAWQ supports high availability NameNode configuration within HDFS.
-
-To maintain cluster health, HAWQ uses a fault tolerance service based on heartbeats and on-demand probe protocols. It can identify newly added nodes dynamically and remove nodes from the cluster when it becomes unusable.
-
-## <a id="abouthighavailability"></a>About High Availability 
-
-HAWQ employs several mechanisms for ensuring high availability. The foremost mechanisms are specific to HAWQ and include the following:
-
-* Master mirroring. Clusters have a standby master in the event of failure of the primary master.
-* Dual clusters. Administrators can create a secondary cluster and synchronizes its data with the primary cluster either through dual ETL or backup and restore mechanisms.
-
-In addition to high availability managed on the HAWQ level, you can enable high availability in HDFS for HAWQ by implementing the high availability feature for NameNodes. See [HAWQ Filespaces and High Availability Enabled HDFS](../admin/HAWQFilespacesandHighAvailabilityEnabledHDFS.html).
-
-
-## <a id="aboutsegmentfailover"></a>About Segment Fault Tolerance 
-
-In HAWQ, the segments are stateless. This ensures faster recovery and better availability.
-
-When a segment fails, the segment is removed from the resource pool. Queries are no longer dispatched to the segment. When the segment is operational again, the Fault Tolerance Service verifies its state and adds the segment back to the resource pool.
-
-## <a id="aboutinterconnectredundancy"></a>About Interconnect Redundancy 
-
-The *interconnect* refers to the inter-process communication between the segments and the network infrastructure on which this communication relies. You can achieve a highly available interconnect by deploying dual Gigabit Ethernet switches on your network and deploying redundant Gigabit connections to the HAWQ host \(master and segment\) servers.
-
-In order to use multiple NICs in HAWQ, NIC bounding is required.

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-title: Resource Management
----
-
-HAWQ provides several approaches to resource management and includes several user-configurable options, including integration with YARN's resource management.
-
-HAWQ has the ability to manage resources by using the following mechanisms:
-
--   Global resource management. You can integrate HAWQ with the YARN resource manager to request or return resources as needed. If you do not integrate HAWQ with YARN, HAWQ exclusively consumes cluster resources and manages its own resources. If you integrate HAWQ with YARN, then HAWQ automatically fetches resources from YARN and manages those obtained resources through its internally defined resource queues. Resources are returned to YARN automatically when resources are not used anymore.
--   User defined hierarchical resource queues. HAWQ administrators or superusers design and define the resource queues used to organize the distribution of resources to queries.
--   Dynamic resource allocation at query runtime. HAWQ dynamically allocates resources based on resource queue definitions. HAWQ automatically distributes resources based on running \(or queued\) queries and resource queue capacities.
--   Resource limitations on virtual segments and queries. You can configure HAWQ to enforce limits on CPU and memory usage both for virtual segments and the resource queues used by queries.
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-For more details on resource management in HAWQ and how it works, see [Managing Resources](../resourcemgmt/HAWQResourceManagement.html).


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