updating docs from master
Project: http://git-wip-us.apache.org/repos/asf/hbase/repo Commit: http://git-wip-us.apache.org/repos/asf/hbase/commit/2e9a55be Tree: http://git-wip-us.apache.org/repos/asf/hbase/tree/2e9a55be Diff: http://git-wip-us.apache.org/repos/asf/hbase/diff/2e9a55be Branch: refs/heads/branch-1.1 Commit: 2e9a55befc308b4892ea5a083412e4f36178ed1a Parents: b6ff374 Author: Nick Dimiduk <[email protected]> Authored: Thu Nov 30 19:53:20 2017 -0800 Committer: Nick Dimiduk <[email protected]> Committed: Thu Nov 30 19:53:20 2017 -0800 ---------------------------------------------------------------------- .../asciidoc/_chapters/appendix_acl_matrix.adoc | 1 + .../appendix_contributing_to_documentation.adoc | 42 +- src/main/asciidoc/_chapters/architecture.adoc | 269 ++++-- src/main/asciidoc/_chapters/asf.adoc | 4 +- src/main/asciidoc/_chapters/backup_restore.adoc | 912 +++++++++++++++++++ src/main/asciidoc/_chapters/community.adoc | 6 +- src/main/asciidoc/_chapters/compression.adoc | 10 +- src/main/asciidoc/_chapters/configuration.adoc | 40 +- src/main/asciidoc/_chapters/cp.adoc | 12 +- src/main/asciidoc/_chapters/datamodel.adoc | 34 +- src/main/asciidoc/_chapters/developer.adoc | 351 ++++--- src/main/asciidoc/_chapters/external_apis.adoc | 27 +- src/main/asciidoc/_chapters/faq.adoc | 4 +- .../asciidoc/_chapters/getting_started.adoc | 6 +- src/main/asciidoc/_chapters/hbase-default.adoc | 52 +- src/main/asciidoc/_chapters/hbase_apis.adoc | 2 +- src/main/asciidoc/_chapters/mapreduce.adoc | 112 ++- src/main/asciidoc/_chapters/ops_mgt.adoc | 159 +++- src/main/asciidoc/_chapters/other_info.adoc | 14 +- src/main/asciidoc/_chapters/performance.adoc | 41 +- src/main/asciidoc/_chapters/preface.adoc | 4 +- src/main/asciidoc/_chapters/protobuf.adoc | 2 +- src/main/asciidoc/_chapters/rpc.adoc | 2 +- src/main/asciidoc/_chapters/schema_design.adoc | 41 +- src/main/asciidoc/_chapters/security.adoc | 12 +- src/main/asciidoc/_chapters/spark.adoc | 4 +- src/main/asciidoc/_chapters/sql.adoc | 4 +- .../_chapters/thrift_filter_language.adoc | 2 +- src/main/asciidoc/_chapters/tracing.adoc | 8 +- .../asciidoc/_chapters/troubleshooting.adoc | 19 +- src/main/asciidoc/_chapters/unit_testing.adoc | 8 +- src/main/asciidoc/_chapters/upgrading.adoc | 166 +++- src/main/asciidoc/_chapters/zookeeper.adoc | 8 +- src/main/asciidoc/book.adoc | 9 +- 34 files changed, 1855 insertions(+), 532 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/hbase/blob/2e9a55be/src/main/asciidoc/_chapters/appendix_acl_matrix.adoc ---------------------------------------------------------------------- diff --git a/src/main/asciidoc/_chapters/appendix_acl_matrix.adoc b/src/main/asciidoc/_chapters/appendix_acl_matrix.adoc index 1d7c748..0c99b1f 100644 --- a/src/main/asciidoc/_chapters/appendix_acl_matrix.adoc +++ b/src/main/asciidoc/_chapters/appendix_acl_matrix.adoc @@ -123,6 +123,7 @@ In case the table goes out of date, the unit tests which check for accuracy of p | | getReplicationPeerConfig | superuser\|global(A) | | updateReplicationPeerConfig | superuser\|global(A) | | listReplicationPeers | superuser\|global(A) +| | getClusterStatus | superuser\|global(A) | Region | openRegion | superuser\|global(A) | | closeRegion | superuser\|global(A) | | flush | superuser\|global(A)\|global\(C)\|TableOwner\|table(A)\|table\(C) http://git-wip-us.apache.org/repos/asf/hbase/blob/2e9a55be/src/main/asciidoc/_chapters/appendix_contributing_to_documentation.adoc ---------------------------------------------------------------------- diff --git a/src/main/asciidoc/_chapters/appendix_contributing_to_documentation.adoc b/src/main/asciidoc/_chapters/appendix_contributing_to_documentation.adoc index 0337182..a603c16 100644 --- a/src/main/asciidoc/_chapters/appendix_contributing_to_documentation.adoc +++ b/src/main/asciidoc/_chapters/appendix_contributing_to_documentation.adoc @@ -35,9 +35,9 @@ including the documentation. In HBase, documentation includes the following areas, and probably some others: -* The link:http://hbase.apache.org/book.html[HBase Reference +* The link:https://hbase.apache.org/book.html[HBase Reference Guide] (this book) -* The link:http://hbase.apache.org/[HBase website] +* The link:https://hbase.apache.org/[HBase website] * API documentation * Command-line utility output and help text * Web UI strings, explicit help text, context-sensitive strings, and others @@ -119,14 +119,14 @@ JIRA and add a version number to the name of the new patch. === Editing the HBase Website -The source for the HBase website is in the HBase source, in the _src/main/site/_ directory. +The source for the HBase website is in the HBase source, in the _src/site/_ directory. Within this directory, source for the individual pages is in the _xdocs/_ directory, and images referenced in those pages are in the _resources/images/_ directory. This directory also stores images used in the HBase Reference Guide. The website's pages are written in an HTML-like XML dialect called xdoc, which has a reference guide at -http://maven.apache.org/archives/maven-1.x/plugins/xdoc/reference/xdocs.html. +https://maven.apache.org/archives/maven-1.x/plugins/xdoc/reference/xdocs.html. You can edit these files in a plain-text editor, an IDE, or an XML editor such as XML Mind XML Editor (XXE) or Oxygen XML Author. @@ -138,23 +138,23 @@ When you are satisfied with your changes, follow the procedure in [[website_publish]] === Publishing the HBase Website and Documentation -HBase uses the ASF's `gitpubsub` mechanism. -. After generating the website and documentation -artifacts using `mvn clean site site:stage`, check out the `asf-site` repository. +HBase uses the ASF's `gitpubsub` mechanism. A Jenkins job runs the +`dev-support/jenkins-scripts/generate-hbase-website.sh` script, which runs the +`mvn clean site site:stage` against the `master` branch of the `hbase` +repository and commits the built artifacts to the `asf-site` branch of the +`hbase-site` repository. When the commit is pushed, the website is redeployed +automatically. If the script encounters an error, an email is sent to the +developer mailing list. You can run the script manually or examine it to see the +steps involved. -. Remove previously-generated content using the following command: -+ ----- -rm -rf rm -rf *apidocs* *book* *.html *.pdf* css js ----- -+ -WARNING: Do not remove the `0.94/` directory. To regenerate them, you must check out -the 0.94 branch and run `mvn clean site site:stage` from there, and then copy the -artifacts to the 0.94/ directory of the `asf-site` branch. - -. Copy the contents of `target/staging` to the branch. +[[website_check_links]] +=== Checking the HBase Website for Broken Links -. Add and commit your changes, and submit a patch for review. +A Jenkins job runs periodically to check HBase website for broken links, using +the `dev-support/jenkins-scripts/check-website-links.sh` script. This script +uses a tool called `linklint` to check for bad links and create a report. If +broken links are found, an email is sent to the developer mailing list. You can +run the script manually or examine it to see the steps involved. === HBase Reference Guide Style Guide and Cheat Sheet @@ -216,7 +216,7 @@ link:http://www.google.com[Google] ---- image::sunset.jpg[Alt Text] ---- -(put the image in the src/main/site/resources/images directory) +(put the image in the src/site/resources/images directory) | An inline image | The image with alt text, as part of the text flow | ---- image:sunset.jpg [Alt Text] @@ -389,7 +389,7 @@ Inline images cannot have titles. They are generally small images like GUI butto image:sunset.jpg[Alt Text] ---- -When doing a local build, save the image to the _src/main/site/resources/images/_ directory. +When doing a local build, save the image to the _src/site/resources/images/_ directory. When you link to the image, do not include the directory portion of the path. The image will be copied to the appropriate target location during the build of the output. http://git-wip-us.apache.org/repos/asf/hbase/blob/2e9a55be/src/main/asciidoc/_chapters/architecture.adoc ---------------------------------------------------------------------- diff --git a/src/main/asciidoc/_chapters/architecture.adoc b/src/main/asciidoc/_chapters/architecture.adoc index 930fa60..9a3cbd9 100644 --- a/src/main/asciidoc/_chapters/architecture.adoc +++ b/src/main/asciidoc/_chapters/architecture.adoc @@ -76,7 +76,7 @@ HBase can run quite well stand-alone on a laptop - but this should be considered [[arch.overview.hbasehdfs]] === What Is The Difference Between HBase and Hadoop/HDFS? -link:http://hadoop.apache.org/hdfs/[HDFS] is a distributed file system that is well suited for the storage of large files. +link:https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html[HDFS] is a distributed file system that is well suited for the storage of large files. Its documentation states that it is not, however, a general purpose file system, and does not provide fast individual record lookups in files. HBase, on the other hand, is built on top of HDFS and provides fast record lookups (and updates) for large tables. This can sometimes be a point of conceptual confusion. @@ -88,31 +88,10 @@ See the <<datamodel>> and the rest of this chapter for more information on how H The catalog table `hbase:meta` exists as an HBase table and is filtered out of the HBase shell's `list` command, but is in fact a table just like any other. -[[arch.catalog.root]] -=== -ROOT- - -NOTE: The `-ROOT-` table was removed in HBase 0.96.0. -Information here should be considered historical. - -The `-ROOT-` table kept track of the location of the `.META` table (the previous name for the table now called `hbase:meta`) prior to HBase 0.96. -The `-ROOT-` table structure was as follows: - -.Key - -* .META. - region key (`.META.,,1`) - -.Values - -* `info:regioninfo` (serialized link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HRegionInfo.html[HRegionInfo] instance of `hbase:meta`) -* `info:server` (server:port of the RegionServer holding `hbase:meta`) -* `info:serverstartcode` (start-time of the RegionServer process holding `hbase:meta`) - [[arch.catalog.meta]] === hbase:meta -The `hbase:meta` table (previously called `.META.`) keeps a list of all regions in the system. -The location of `hbase:meta` was previously tracked within the `-ROOT-` table, but is now stored in ZooKeeper. +The `hbase:meta` table (previously called `.META.`) keeps a list of all regions in the system, and the location of `hbase:meta` is stored in ZooKeeper. The `hbase:meta` table structure is as follows: @@ -122,7 +101,7 @@ The `hbase:meta` table structure is as follows: .Values -* `info:regioninfo` (serialized link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HRegionInfo.html[HRegionInfo] instance for this region) +* `info:regioninfo` (serialized link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HRegionInfo.html[HRegionInfo] instance for this region) * `info:server` (server:port of the RegionServer containing this region) * `info:serverstartcode` (start-time of the RegionServer process containing this region) @@ -140,9 +119,7 @@ If a region has both an empty start and an empty end key, it is the only region ==== In the (hopefully unlikely) event that programmatic processing of catalog metadata -is required, see the -+++<a href="http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/util/Writables.html#getHRegionInfo%28byte%5B%5D%29";>Writables</a>+++ -utility. +is required, see the link:https://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/client/RegionInfo.html#parseFrom-byte:A-[RegionInfo.parseFrom] utility. [[arch.catalog.startup]] === Startup Sequencing @@ -164,7 +141,7 @@ Should a region be reassigned either by the master load balancer or because a Re See <<master.runtime>> for more information about the impact of the Master on HBase Client communication. -Administrative functions are done via an instance of link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Admin.html[Admin] +Administrative functions are done via an instance of link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Admin.html[Admin] [[client.connections]] === Cluster Connections @@ -180,12 +157,12 @@ Finally, be sure to cleanup your `Connection` instance before exiting. `Connections` are heavyweight objects but thread-safe so you can create one for your application and keep the instance around. `Table`, `Admin` and `RegionLocator` instances are lightweight. Create as you go and then let go as soon as you are done by closing them. -See the link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/package-summary.html[Client Package Javadoc Description] for example usage of the new HBase 1.0 API. +See the link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/package-summary.html[Client Package Javadoc Description] for example usage of the new HBase 1.0 API. ==== API before HBase 1.0.0 -Instances of `HTable` are the way to interact with an HBase cluster earlier than 1.0.0. _link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html[Table] instances are not thread-safe_. Only one thread can use an instance of Table at any given time. -When creating Table instances, it is advisable to use the same link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HBaseConfiguration[HBaseConfiguration] instance. +Instances of `HTable` are the way to interact with an HBase cluster earlier than 1.0.0. _link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html[Table] instances are not thread-safe_. Only one thread can use an instance of Table at any given time. +When creating Table instances, it is advisable to use the same link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HBaseConfiguration[HBaseConfiguration] instance. This will ensure sharing of ZooKeeper and socket instances to the RegionServers which is usually what you want. For example, this is preferred: @@ -206,7 +183,7 @@ HBaseConfiguration conf2 = HBaseConfiguration.create(); HTable table2 = new HTable(conf2, "myTable"); ---- -For more information about how connections are handled in the HBase client, see link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/ConnectionFactory.html[ConnectionFactory]. +For more information about how connections are handled in the HBase client, see link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/ConnectionFactory.html[ConnectionFactory]. [[client.connection.pooling]] ===== Connection Pooling @@ -230,19 +207,19 @@ try (Connection connection = ConnectionFactory.createConnection(conf); [WARNING] ==== Previous versions of this guide discussed `HTablePool`, which was deprecated in HBase 0.94, 0.95, and 0.96, and removed in 0.98.1, by link:https://issues.apache.org/jira/browse/HBASE-6580[HBASE-6580], or `HConnection`, which is deprecated in HBase 1.0 by `Connection`. -Please use link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Connection.html[Connection] instead. +Please use link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Connection.html[Connection] instead. ==== [[client.writebuffer]] === WriteBuffer and Batch Methods -In HBase 1.0 and later, link:http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/client/HTable.html[HTable] is deprecated in favor of link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html[Table]. `Table` does not use autoflush. To do buffered writes, use the BufferedMutator class. +In HBase 1.0 and later, link:https://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/client/HTable.html[HTable] is deprecated in favor of link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html[Table]. `Table` does not use autoflush. To do buffered writes, use the BufferedMutator class. -Before a `Table` or `HTable` instance is discarded, invoke either `close()` or `flushCommits()`, so `Put`s will not be lost. +In HBase 2.0 and later, link:https://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/client/HTable.html[HTable] does not use BufferedMutator to execute the ``Put`` operation. Refer to link:https://issues.apache.org/jira/browse/HBASE-18500[HBASE-18500] for more information. For additional information on write durability, review the link:/acid-semantics.html[ACID semantics] page. -For fine-grained control of batching of ``Put``s or ``Delete``s, see the link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html#batch%28java.util.List%29[batch] methods on Table. +For fine-grained control of batching of ``Put``s or ``Delete``s, see the link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html#batch-java.util.List-java.lang.Object:A-[batch] methods on Table. [[async.client]] === Asynchronous Client === @@ -259,11 +236,11 @@ There are several differences for scan: * There is a `scanAll` method which will return all the results at once. It aims to provide a simpler way for small scans which you want to get the whole results at once usually. * The Observer Pattern. There is a scan method which accepts a `ScanResultConsumer` as a parameter. It will pass the results to the consumer. -Notice that there are two types of asynchronous table, one is `AsyncTable` and the other is `RawAsyncTable`. +Notice that `AsyncTable` interface is templatized. The template parameter specifies the type of `ScanResultConsumerBase` used by scans, which means the observer style scan APIs are different. The two types of scan consumers are - `ScanResultConsumer` and `AdvancedScanResultConsumer`. -For `AsyncTable`, you need to provide a thread pool when getting it. The callbacks registered to the returned CompletableFuture will be executed in that thread pool. It is designed for normal users. You are free to do anything in the callbacks. +`ScanResultConsumer` needs a separate thread pool which is used to execute the callbacks registered to the returned CompletableFuture. Because the use of separate thread pool frees up RPC threads, callbacks are free to do anything. Use this if the callbacks are not quick, or when in doubt. -For `RawAsyncTable`, all the callbacks are executed inside the framework thread so it is not allowed to do time consuming works in the callbacks otherwise you may block the framework thread and cause very bad performance impact. It is designed for advanced users who want to write high performance code. You can see the `org.apache.hadoop.hbase.client.example.HttpProxyExample` to see how to write fully asynchronous code with `RawAsyncTable`. And coprocessor related methods are only in `RawAsyncTable`. +`AdvancedScanResultConsumer` executes callbacks inside the framework thread. It is not allowed to do time consuming work in the callbacks else it will likely block the framework threads and cause very bad performance impact. As its name, it is designed for advanced users who want to write high performance code. See `org.apache.hadoop.hbase.client.example.HttpProxyExample` for how to write fully asynchronous code with it. [[async.admin]] === Asynchronous Admin === @@ -286,7 +263,7 @@ Information on non-Java clients and custom protocols is covered in <<external_ap [[client.filter]] == Client Request Filters -link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Get.html[Get] and link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html[Scan] instances can be optionally configured with link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/Filter.html[filters] which are applied on the RegionServer. +link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Get.html[Get] and link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html[Scan] instances can be optionally configured with link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/Filter.html[filters] which are applied on the RegionServer. Filters can be confusing because there are many different types, and it is best to approach them by understanding the groups of Filter functionality. @@ -298,7 +275,7 @@ Structural Filters contain other Filters. [[client.filter.structural.fl]] ==== FilterList -link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/FilterList.html[FilterList] represents a list of Filters with a relationship of `FilterList.Operator.MUST_PASS_ALL` or `FilterList.Operator.MUST_PASS_ONE` between the Filters. +link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/FilterList.html[FilterList] represents a list of Filters with a relationship of `FilterList.Operator.MUST_PASS_ALL` or `FilterList.Operator.MUST_PASS_ONE` between the Filters. The following example shows an 'or' between two Filters (checking for either 'my value' or 'my other value' on the same attribute). [source,java] @@ -307,14 +284,14 @@ FilterList list = new FilterList(FilterList.Operator.MUST_PASS_ONE); SingleColumnValueFilter filter1 = new SingleColumnValueFilter( cf, column, - CompareOp.EQUAL, + CompareOperator.EQUAL, Bytes.toBytes("my value") ); list.add(filter1); SingleColumnValueFilter filter2 = new SingleColumnValueFilter( cf, column, - CompareOp.EQUAL, + CompareOperator.EQUAL, Bytes.toBytes("my other value") ); list.add(filter2); @@ -328,9 +305,9 @@ scan.setFilter(list); ==== SingleColumnValueFilter A SingleColumnValueFilter (see: -http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/SingleColumnValueFilter.html) -can be used to test column values for equivalence (`CompareOp.EQUAL`), -inequality (`CompareOp.NOT_EQUAL`), or ranges (e.g., `CompareOp.GREATER`). The following is an +https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/SingleColumnValueFilter.html) +can be used to test column values for equivalence (`CompareOperaor.EQUAL`), +inequality (`CompareOperaor.NOT_EQUAL`), or ranges (e.g., `CompareOperaor.GREATER`). The following is an example of testing equivalence of a column to a String value "my value"... [source,java] @@ -338,7 +315,7 @@ example of testing equivalence of a column to a String value "my value"... SingleColumnValueFilter filter = new SingleColumnValueFilter( cf, column, - CompareOp.EQUAL, + CompareOperaor.EQUAL, Bytes.toBytes("my value") ); scan.setFilter(filter); @@ -353,7 +330,7 @@ These Comparators are used in concert with other Filters, such as <<client.filte [[client.filter.cvp.rcs]] ==== RegexStringComparator -link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/RegexStringComparator.html[RegexStringComparator] supports regular expressions for value comparisons. +link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/RegexStringComparator.html[RegexStringComparator] supports regular expressions for value comparisons. [source,java] ---- @@ -361,7 +338,7 @@ RegexStringComparator comp = new RegexStringComparator("my."); // any value th SingleColumnValueFilter filter = new SingleColumnValueFilter( cf, column, - CompareOp.EQUAL, + CompareOperaor.EQUAL, comp ); scan.setFilter(filter); @@ -372,7 +349,7 @@ See the Oracle JavaDoc for link:http://download.oracle.com/javase/6/docs/api/jav [[client.filter.cvp.substringcomparator]] ==== SubstringComparator -link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/SubstringComparator.html[SubstringComparator] can be used to determine if a given substring exists in a value. +link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/SubstringComparator.html[SubstringComparator] can be used to determine if a given substring exists in a value. The comparison is case-insensitive. [source,java] @@ -382,7 +359,7 @@ SubstringComparator comp = new SubstringComparator("y val"); // looking for 'm SingleColumnValueFilter filter = new SingleColumnValueFilter( cf, column, - CompareOp.EQUAL, + CompareOperaor.EQUAL, comp ); scan.setFilter(filter); @@ -391,12 +368,12 @@ scan.setFilter(filter); [[client.filter.cvp.bfp]] ==== BinaryPrefixComparator -See link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/BinaryPrefixComparator.html[BinaryPrefixComparator]. +See link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/BinaryPrefixComparator.html[BinaryPrefixComparator]. [[client.filter.cvp.bc]] ==== BinaryComparator -See link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/BinaryComparator.html[BinaryComparator]. +See link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/BinaryComparator.html[BinaryComparator]. [[client.filter.kvm]] === KeyValue Metadata @@ -406,18 +383,18 @@ As HBase stores data internally as KeyValue pairs, KeyValue Metadata Filters eva [[client.filter.kvm.ff]] ==== FamilyFilter -link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/FamilyFilter.html[FamilyFilter] can be used to filter on the ColumnFamily. +link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/FamilyFilter.html[FamilyFilter] can be used to filter on the ColumnFamily. It is generally a better idea to select ColumnFamilies in the Scan than to do it with a Filter. [[client.filter.kvm.qf]] ==== QualifierFilter -link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/QualifierFilter.html[QualifierFilter] can be used to filter based on Column (aka Qualifier) name. +link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/QualifierFilter.html[QualifierFilter] can be used to filter based on Column (aka Qualifier) name. [[client.filter.kvm.cpf]] ==== ColumnPrefixFilter -link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/ColumnPrefixFilter.html[ColumnPrefixFilter] can be used to filter based on the lead portion of Column (aka Qualifier) names. +link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/ColumnPrefixFilter.html[ColumnPrefixFilter] can be used to filter based on the lead portion of Column (aka Qualifier) names. A ColumnPrefixFilter seeks ahead to the first column matching the prefix in each row and for each involved column family. It can be used to efficiently get a subset of the columns in very wide rows. @@ -450,7 +427,7 @@ rs.close(); [[client.filter.kvm.mcpf]] ==== MultipleColumnPrefixFilter -link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/MultipleColumnPrefixFilter.html[MultipleColumnPrefixFilter] behaves like ColumnPrefixFilter but allows specifying multiple prefixes. +link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/MultipleColumnPrefixFilter.html[MultipleColumnPrefixFilter] behaves like ColumnPrefixFilter but allows specifying multiple prefixes. Like ColumnPrefixFilter, MultipleColumnPrefixFilter efficiently seeks ahead to the first column matching the lowest prefix and also seeks past ranges of columns between prefixes. It can be used to efficiently get discontinuous sets of columns from very wide rows. @@ -480,7 +457,7 @@ rs.close(); [[client.filter.kvm.crf]] ==== ColumnRangeFilter -A link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/ColumnRangeFilter.html[ColumnRangeFilter] allows efficient intra row scanning. +A link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/ColumnRangeFilter.html[ColumnRangeFilter] allows efficient intra row scanning. A ColumnRangeFilter can seek ahead to the first matching column for each involved column family. It can be used to efficiently get a 'slice' of the columns of a very wide row. @@ -521,7 +498,7 @@ Note: Introduced in HBase 0.92 [[client.filter.row.rf]] ==== RowFilter -It is generally a better idea to use the startRow/stopRow methods on Scan for row selection, however link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/RowFilter.html[RowFilter] can also be used. +It is generally a better idea to use the startRow/stopRow methods on Scan for row selection, however link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/RowFilter.html[RowFilter] can also be used. [[client.filter.utility]] === Utility @@ -530,7 +507,7 @@ It is generally a better idea to use the startRow/stopRow methods on Scan for ro ==== FirstKeyOnlyFilter This is primarily used for rowcount jobs. -See link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/FirstKeyOnlyFilter.html[FirstKeyOnlyFilter]. +See link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/FirstKeyOnlyFilter.html[FirstKeyOnlyFilter]. [[architecture.master]] == Master @@ -580,7 +557,7 @@ See <<regions.arch.assignment>> for more information on region assignment. ==== CatalogJanitor Periodically checks and cleans up the `hbase:meta` table. -See <arch.catalog.meta>> for more information on the meta table. +See <<arch.catalog.meta>> for more information on the meta table. [[regionserver.arch]] == RegionServer @@ -657,7 +634,7 @@ However, latencies tend to be less erratic across time, because there is less ga If the BucketCache is deployed in off-heap mode, this memory is not managed by the GC at all. This is why you'd use BucketCache, so your latencies are less erratic and to mitigate GCs and heap fragmentation. See Nick Dimiduk's link:http://www.n10k.com/blog/blockcache-101/[BlockCache 101] for comparisons running on-heap vs off-heap tests. -Also see link:http://people.apache.org/~stack/bc/[Comparing BlockCache Deploys] which finds that if your dataset fits inside your LruBlockCache deploy, use it otherwise if you are experiencing cache churn (or you want your cache to exist beyond the vagaries of java GC), use BucketCache. +Also see link:https://people.apache.org/~stack/bc/[Comparing BlockCache Deploys] which finds that if your dataset fits inside your LruBlockCache deploy, use it otherwise if you are experiencing cache churn (or you want your cache to exist beyond the vagaries of java GC), use BucketCache. When you enable BucketCache, you are enabling a two tier caching system, an L1 cache which is implemented by an instance of LruBlockCache and an off-heap L2 cache which is implemented by BucketCache. Management of these two tiers and the policy that dictates how blocks move between them is done by `CombinedBlockCache`. @@ -668,7 +645,7 @@ See <<offheap.blockcache>> for more detail on going off-heap. ==== General Cache Configurations Apart from the cache implementation itself, you can set some general configuration options to control how the cache performs. -See http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/io/hfile/CacheConfig.html. +See https://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/io/hfile/CacheConfig.html. After setting any of these options, restart or rolling restart your cluster for the configuration to take effect. Check logs for errors or unexpected behavior. @@ -730,9 +707,9 @@ Your data is not the only resident of the block cache. Here are others that you may have to take into account: Catalog Tables:: - The `-ROOT-` (prior to HBase 0.96, see <<arch.catalog.root,arch.catalog.root>>) and `hbase:meta` tables are forced into the block cache and have the in-memory priority which means that they are harder to evict. - The former never uses more than a few hundred bytes while the latter can occupy a few MBs - (depending on the number of regions). + The `hbase:meta` table is forced into the block cache and have the in-memory priority which means that they are harder to evict. + +NOTE: The hbase:meta tables can occupy a few MBs depending on the number of regions. HFiles Indexes:: An _HFile_ is the file format that HBase uses to store data in HDFS. @@ -778,7 +755,7 @@ Since link:https://issues.apache.org/jira/browse/HBASE-4683[HBASE-4683 Always ca ===== How to Enable BucketCache The usual deploy of BucketCache is via a managing class that sets up two caching tiers: an L1 on-heap cache implemented by LruBlockCache and a second L2 cache implemented with BucketCache. -The managing class is link:http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/io/hfile/CombinedBlockCache.html[CombinedBlockCache] by default. +The managing class is link:https://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/io/hfile/CombinedBlockCache.html[CombinedBlockCache] by default. The previous link describes the caching 'policy' implemented by CombinedBlockCache. In short, it works by keeping meta blocks -- INDEX and BLOOM in the L1, on-heap LruBlockCache tier -- and DATA blocks are kept in the L2, BucketCache tier. It is possible to amend this behavior in HBase since version 1.0 and ask that a column family have both its meta and DATA blocks hosted on-heap in the L1 tier by setting `cacheDataInL1` via `(HColumnDescriptor.setCacheDataInL1(true)` or in the shell, creating or amending column families setting `CACHE_DATA_IN_L1` to true: e.g. @@ -904,7 +881,7 @@ The compressed BlockCache is disabled by default. To enable it, set `hbase.block As write requests are handled by the region server, they accumulate in an in-memory storage system called the _memstore_. Once the memstore fills, its content are written to disk as additional store files. This event is called a _memstore flush_. As store files accumulate, the RegionServer will <<compaction,compact>> them into fewer, larger files. After each flush or compaction finishes, the amount of data stored in the region has changed. The RegionServer consults the region split policy to determine if the region has grown too large or should be split for another policy-specific reason. A region split request is enqueued if the policy recommends it. -Logically, the process of splitting a region is simple. We find a suitable point in the keyspace of the region where we should divide the region in half, then split the region's data into two new regions at that point. The details of the process however are not simple. When a split happens, the newly created _daughter regions_ do not rewrite all the data into new files immediately. Instead, they create small files similar to symbolic link files, named link:http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/io/Reference.html[Reference files], which point to either the top or bottom part of the parent store file according to the split point. The reference file is used just like a regular data file, but only half of the records are considered. The region can only be split if there are no more references to the immutable data files of the parent region. Those reference files are cleaned gradually by compactions, so that the region will stop referring to its parents files, and c an be split further. +Logically, the process of splitting a region is simple. We find a suitable point in the keyspace of the region where we should divide the region in half, then split the region's data into two new regions at that point. The details of the process however are not simple. When a split happens, the newly created _daughter regions_ do not rewrite all the data into new files immediately. Instead, they create small files similar to symbolic link files, named link:https://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/io/Reference.html[Reference files], which point to either the top or bottom part of the parent store file according to the split point. The reference file is used just like a regular data file, but only half of the records are considered. The region can only be split if there are no more references to the immutable data files of the parent region. Those reference files are cleaned gradually by compactions, so that the region will stop referring to its parents files, and can be split further. Although splitting the region is a local decision made by the RegionServer, the split process itself must coordinate with many actors. The RegionServer notifies the Master before and after the split, updates the `.META.` table so that clients can discover the new daughter regions, and rearranges the directory structure and data files in HDFS. Splitting is a multi-task process. To enable rollback in case of an error, the RegionServer keeps an in-memory journal about the execution state. The steps taken by the RegionServer to execute the split are illustrated in <<regionserver_split_process_image>>. Each step is labeled with its step number. Actions from RegionServers or Master are shown in red, while actions from the clients are show in green. @@ -938,7 +915,7 @@ Under normal operations, the WAL is not needed because data changes move from th However, if a RegionServer crashes or becomes unavailable before the MemStore is flushed, the WAL ensures that the changes to the data can be replayed. If writing to the WAL fails, the entire operation to modify the data fails. -HBase uses an implementation of the link:http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/wal/WAL.html[WAL] interface. +HBase uses an implementation of the link:https://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/wal/WAL.html[WAL] interface. Usually, there is only one instance of a WAL per RegionServer. The RegionServer records Puts and Deletes to it, before recording them to the <<store.memstore>> for the affected <<store>>. @@ -1191,30 +1168,21 @@ Due to an asynchronous implementation, in very rare cases, the split log manager For that reason, it periodically checks for remaining uncompleted task in its task map or ZooKeeper. If none are found, it throws an exception so that the log splitting can be retried right away instead of hanging there waiting for something that won't happen. +[[wal.compression]] +==== WAL Compression ==== -[[distributed.log.replay]] -====== Distributed Log Replay - -After a RegionServer fails, its failed regions are assigned to another RegionServer, which are marked as "recovering" in ZooKeeper. -A split log worker directly replays edits from the WAL of the failed RegionServer to the regions at its new location. -When a region is in "recovering" state, it can accept writes but no reads (including Append and Increment), region splits or merges. - -Distributed Log Replay extends the <<distributed.log.splitting>> framework. -It works by directly replaying WAL edits to another RegionServer instead of creating _recovered.edits_ files. -It provides the following advantages over distributed log splitting alone: - -* It eliminates the overhead of writing and reading a large number of _recovered.edits_ files. - It is not unusual for thousands of _recovered.edits_ files to be created and written concurrently during a RegionServer recovery. - Many small random writes can degrade overall system performance. -* It allows writes even when a region is in recovering state. - It only takes seconds for a recovering region to accept writes again. +The content of the WAL can be compressed using LRU Dictionary compression. +This can be used to speed up WAL replication to different datanodes. +The dictionary can store up to 2^15^ elements; eviction starts after this number is exceeded. -.Enabling Distributed Log Replay -To enable distributed log replay, set `hbase.master.distributed.log.replay` to `true`. -This will be the default for HBase 0.99 (link:https://issues.apache.org/jira/browse/HBASE-10888[HBASE-10888]). +To enable WAL compression, set the `hbase.regionserver.wal.enablecompression` property to `true`. +The default value for this property is `false`. +By default, WAL tag compression is turned on when WAL compression is enabled. +You can turn off WAL tag compression by setting the `hbase.regionserver.wal.tags.enablecompression` property to 'false'. -You must also enable HFile version 3 (which is the default HFile format starting in HBase 0.99. -See link:https://issues.apache.org/jira/browse/HBASE-10855[HBASE-10855]). Distributed log replay is unsafe for rolling upgrades. +A possible downside to WAL compression is that we lose more data from the last block in the WAL if it ill-terminated +mid-write. If entries in this last block were added with new dictionary entries but we failed persist the amended +dictionary because of an abrupt termination, a read of this last block may not be able to resolve last-written entries. [[wal.disable]] ==== Disabling the WAL @@ -1396,12 +1364,12 @@ The HDFS client does the following by default when choosing locations to write r . Second replica is written to a random node on another rack . Third replica is written on the same rack as the second, but on a different node chosen randomly . Subsequent replicas are written on random nodes on the cluster. - See _Replica Placement: The First Baby Steps_ on this page: link:http://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html[HDFS Architecture] + See _Replica Placement: The First Baby Steps_ on this page: link:https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html[HDFS Architecture] Thus, HBase eventually achieves locality for a region after a flush or a compaction. In a RegionServer failover situation a RegionServer may be assigned regions with non-local StoreFiles (because none of the replicas are local), however as new data is written in the region, or the table is compacted and StoreFiles are re-written, they will become "local" to the RegionServer. -For more information, see _Replica Placement: The First Baby Steps_ on this page: link:http://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html[HDFS Architecture] and also Lars George's blog on link:http://www.larsgeorge.com/2010/05/hbase-file-locality-in-hdfs.html[HBase and HDFS locality]. +For more information, see _Replica Placement: The First Baby Steps_ on this page: link:https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html[HDFS Architecture] and also Lars George's blog on link:http://www.larsgeorge.com/2010/05/hbase-file-locality-in-hdfs.html[HBase and HDFS locality]. [[arch.region.splits]] === Region Splits @@ -1416,9 +1384,9 @@ See <<disable.splitting>> for how to manually manage splits (and for why you mig ==== Custom Split Policies You can override the default split policy using a custom -link:http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/regionserver/RegionSplitPolicy.html[RegionSplitPolicy](HBase 0.94+). +link:https://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/regionserver/RegionSplitPolicy.html[RegionSplitPolicy](HBase 0.94+). Typically a custom split policy should extend HBase's default split policy: -link:http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/regionserver/IncreasingToUpperBoundRegionSplitPolicy.html[IncreasingToUpperBoundRegionSplitPolicy]. +link:https://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/regionserver/IncreasingToUpperBoundRegionSplitPolicy.html[IncreasingToUpperBoundRegionSplitPolicy]. The policy can set globally through the HBase configuration or on a per-table basis. @@ -1492,13 +1460,13 @@ Using a Custom Algorithm:: As parameters, you give it the algorithm, desired number of regions, and column families. It includes two split algorithms. The first is the - `link:http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/util/RegionSplitter.HexStringSplit.html[HexStringSplit]` + `link:https://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/util/RegionSplitter.HexStringSplit.html[HexStringSplit]` algorithm, which assumes the row keys are hexadecimal strings. The second, - `link:http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/util/RegionSplitter.UniformSplit.html[UniformSplit]`, + `link:https://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/util/RegionSplitter.UniformSplit.html[UniformSplit]`, assumes the row keys are random byte arrays. You will probably need to develop your own - `link:http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/util/RegionSplitter.SplitAlgorithm.html[SplitAlgorithm]`, + `link:https://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/util/RegionSplitter.SplitAlgorithm.html[SplitAlgorithm]`, using the provided ones as models. === Online Region Merges @@ -1574,7 +1542,7 @@ StoreFiles are where your data lives. ===== HFile Format -The _HFile_ file format is based on the SSTable file described in the link:http://research.google.com/archive/bigtable.html[BigTable [2006]] paper and on Hadoop's link:http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/file/tfile/TFile.html[TFile] (The unit test suite and the compression harness were taken directly from TFile). Schubert Zhang's blog post on link:http://cloudepr.blogspot.com/2009/09/hfile-block-indexed-file-format-to.html[HFile: A Block-Indexed File Format to Store Sorted Key-Value Pairs] makes for a thorough introduction to HBase's HFile. +The _HFile_ file format is based on the SSTable file described in the link:http://research.google.com/archive/bigtable.html[BigTable [2006]] paper and on Hadoop's link:https://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/file/tfile/TFile.html[TFile] (The unit test suite and the compression harness were taken directly from TFile). Schubert Zhang's blog post on link:http://cloudepr.blogspot.com/2009/09/hfile-block-indexed-file-format-to.html[HFile: A Block-Indexed File Format to Store Sorted Key-Value Pairs] makes for a thorough introduction to HBase's HFile. Matteo Bertozzi has also put up a helpful description, link:http://th30z.blogspot.com/2011/02/hbase-io-hfile.html?spref=tw[HBase I/O: HFile]. For more information, see the HFile source code. @@ -2086,6 +2054,107 @@ Why? NOTE: This information is now included in the configuration parameter table in <<compaction.parameters>>. +[[ops.date.tiered]] +===== Date Tiered Compaction + +Date tiered compaction is a date-aware store file compaction strategy that is beneficial for time-range scans for time-series data. + +[[ops.date.tiered.when]] +===== When To Use Date Tiered Compactions + +Consider using Date Tiered Compaction for reads for limited time ranges, especially scans of recent data + +Don't use it for + +* random gets without a limited time range +* frequent deletes and updates +* Frequent out of order data writes creating long tails, especially writes with future timestamps +* frequent bulk loads with heavily overlapping time ranges + +.Performance Improvements +Performance testing has shown that the performance of time-range scans improve greatly for limited time ranges, especially scans of recent data. + +[[ops.date.tiered.enable]] +====== Enabling Date Tiered Compaction + +You can enable Date Tiered compaction for a table or a column family, by setting its `hbase.hstore.engine.class` to `org.apache.hadoop.hbase.regionserver.DateTieredStoreEngine`. + +You also need to set `hbase.hstore.blockingStoreFiles` to a high number, such as 60, if using all default settings, rather than the default value of 12). Use 1.5~2 x projected file count if changing the parameters, Projected file count = windows per tier x tier count + incoming window min + files older than max age + +You also need to set `hbase.hstore.compaction.max` to the same value as `hbase.hstore.blockingStoreFiles` to unblock major compaction. + +.Procedure: Enable Date Tiered Compaction +. Run one of following commands in the HBase shell. + Replace the table name `orders_table` with the name of your table. ++ +[source,sql] +---- +alter 'orders_table', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.DateTieredStoreEngine', 'hbase.hstore.blockingStoreFiles' => '60', 'hbase.hstore.compaction.min'=>'2', 'hbase.hstore.compaction.max'=>'60'} +alter 'orders_table', {NAME => 'blobs_cf', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.DateTieredStoreEngine', 'hbase.hstore.blockingStoreFiles' => '60', 'hbase.hstore.compaction.min'=>'2', 'hbase.hstore.compaction.max'=>'60'}} +create 'orders_table', 'blobs_cf', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.DateTieredStoreEngine', 'hbase.hstore.blockingStoreFiles' => '60', 'hbase.hstore.compaction.min'=>'2', 'hbase.hstore.compaction.max'=>'60'} +---- + +. Configure other options if needed. + See <<ops.date.tiered.config>> for more information. + +.Procedure: Disable Date Tiered Compaction +. Set the `hbase.hstore.engine.class` option to either nil or `org.apache.hadoop.hbase.regionserver.DefaultStoreEngine`. + Either option has the same effect. + Make sure you set the other options you changed to the original settings too. ++ +[source,sql] +---- +alter 'orders_table', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.DefaultStoreEngine'ï¼ 'hbase.hstore.blockingStoreFiles' => '12', 'hbase.hstore.compaction.min'=>'6', 'hbase.hstore.compaction.max'=>'12'}} +---- + +When you change the store engine either way, a major compaction will likely be performed on most regions. +This is not necessary on new tables. + +[[ops.date.tiered.config]] +====== Configuring Date Tiered Compaction + +Each of the settings for date tiered compaction should be configured at the table or column family level. +If you use HBase shell, the general command pattern is as follows: + +[source,sql] +---- +alter 'orders_table', CONFIGURATION => {'key' => 'value', ..., 'key' => 'value'}} +---- + +[[ops.date.tiered.config.parameters]] +.Tier Parameters + +You can configure your date tiers by changing the settings for the following parameters: + +.Date Tier Parameters +[cols="1,1a", frame="all", options="header"] +|=== +| Setting +| Notes + +|`hbase.hstore.compaction.date.tiered.max.storefile.age.millis` +|Files with max-timestamp smaller than this will no longer be compacted.Default at Long.MAX_VALUE. + +| `hbase.hstore.compaction.date.tiered.base.window.millis` +| Base window size in milliseconds. Default at 6 hours. + +| `hbase.hstore.compaction.date.tiered.windows.per.tier` +| Number of windows per tier. Default at 4. + +| `hbase.hstore.compaction.date.tiered.incoming.window.min` +| Minimal number of files to compact in the incoming window. Set it to expected number of files in the window to avoid wasteful compaction. Default at 6. + +| `hbase.hstore.compaction.date.tiered.window.policy.class` +| The policy to select store files within the same time window. It doesnât apply to the incoming window. Default at exploring compaction. This is to avoid wasteful compaction. +|=== + +[[ops.date.tiered.config.compaction.throttler]] +.Compaction Throttler + +With tiered compaction all servers in the cluster will promote windows to higher tier at the same time, so using a compaction throttle is recommended: +Set `hbase.regionserver.throughput.controller` to `org.apache.hadoop.hbase.regionserver.compactions.PressureAwareCompactionThroughputController`. + +NOTE: For more information about date tiered compaction, please refer to the design specification at https://docs.google.com/document/d/1_AmlNb2N8Us1xICsTeGDLKIqL6T-oHoRLZ323MG_uy8 [[ops.stripe]] ===== Experimental: Stripe Compactions @@ -2299,7 +2368,7 @@ See the `LoadIncrementalHFiles` class for more information. As HBase runs on HDFS (and each StoreFile is written as a file on HDFS), it is important to have an understanding of the HDFS Architecture especially in terms of how it stores files, handles failovers, and replicates blocks. -See the Hadoop documentation on link:http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html[HDFS Architecture] for more information. +See the Hadoop documentation on link:https://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html[HDFS Architecture] for more information. [[arch.hdfs.nn]] === NameNode @@ -2703,7 +2772,7 @@ if (result.isStale()) { === Resources . More information about the design and implementation can be found at the jira issue: link:https://issues.apache.org/jira/browse/HBASE-10070[HBASE-10070] -. HBaseCon 2014 link:http://hbasecon.com/sessions/#session15[talk] also contains some details and link:http://www.slideshare.net/enissoz/hbase-high-availability-for-reads-with-time[slides]. +. HBaseCon 2014 talk: link:https://hbase.apache.org/www.hbasecon.com/#2014-PresentationsRecordings[HBase Read High Availability Using Timeline-Consistent Region Replicas] also contains some details and link:http://www.slideshare.net/enissoz/hbase-high-availability-for-reads-with-time[slides]. ifdef::backend-docbook[] [index] http://git-wip-us.apache.org/repos/asf/hbase/blob/2e9a55be/src/main/asciidoc/_chapters/asf.adoc ---------------------------------------------------------------------- diff --git a/src/main/asciidoc/_chapters/asf.adoc b/src/main/asciidoc/_chapters/asf.adoc index 47c29e5..18cf95a 100644 --- a/src/main/asciidoc/_chapters/asf.adoc +++ b/src/main/asciidoc/_chapters/asf.adoc @@ -35,13 +35,13 @@ HBase is a project in the Apache Software Foundation and as such there are respo [[asf.devprocess]] === ASF Development Process -See the link:http://www.apache.org/dev/#committers[Apache Development Process page] for all sorts of information on how the ASF is structured (e.g., PMC, committers, contributors), to tips on contributing and getting involved, and how open-source works at ASF. +See the link:https://www.apache.org/dev/#committers[Apache Development Process page] for all sorts of information on how the ASF is structured (e.g., PMC, committers, contributors), to tips on contributing and getting involved, and how open-source works at ASF. [[asf.reporting]] === ASF Board Reporting Once a quarter, each project in the ASF portfolio submits a report to the ASF board. This is done by the HBase project lead and the committers. -See link:http://www.apache.org/foundation/board/reporting[ASF board reporting] for more information. +See link:https://www.apache.org/foundation/board/reporting[ASF board reporting] for more information. :numbered:
