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+// Licensed to the Apache Software Foundation (ASF) under one or more
+// contributor license agreements. See the NOTICE file distributed with
+// this work for additional information regarding copyright ownership.
+// The ASF licenses this file to You under the Apache License, Version 2.0
+// (the "License"); you may not use this file except in compliance with
+// the License. You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+== SSL
+Accumulo, through Thrift's TSSLTransport, provides the ability to encrypt
+wire communication between Accumulo servers and clients using secure
+sockets layer (SSL). SSL certifcates signed by the same certificate authority
+control the "circle of trust" in which a secure connection can be established.
+Typically, each host running Accumulo processes would be given a certificate
+which identifies itself.
+
+Clients can optionally also be given a certificate, when client-auth is 
enabled,
+which prevents unwanted clients from accessing the system. The SSL integration
+presently provides no authentication support within Accumulo (an Accumulo 
username
+and password are still required) and is only used to establish a means for
+secure communication.
+
+=== Server configuration
+
+As previously mentioned, the circle of trust is established by the certificate
+authority which created the certificates in use. Because of the tight coupling
+of certificate generation with an organization's policies, Accumulo does not
+provide a method in which to automatically create the necessary SSL components.
+
+Administrators without existing infrastructure built on SSL are encourage to
+use OpenSSL and the +keytool+ command. An example of these commands are
+included in a section below. Accumulo servers require a certificate and 
keystore,
+in the form of Java KeyStores, to enable SSL. The following configuration 
assumes
+these files already exist.
+
+In +accumulo-site.xml+, the following properties are required:
+
+* *rpc.javax.net.ssl.keyStore*=_The path on the local filesystem to the 
keystore containing the server's certificate_
+* *rpc.javax.net.ssl.keyStorePassword*=_The password for the keystore 
containing the server's certificate_
+* *rpc.javax.net.ssl.trustStore*=_The path on the local filesystem to the 
keystore containing the certificate authority's public key_
+* *rpc.javax.net.ssl.trustStorePassword*=_The password for the keystore 
containing the certificate authority's public key_
+* *instance.rpc.ssl.enabled*=_true_
+
+Optionally, SSL client-authentication (two-way SSL) can also be enabled by 
setting
++instance.rpc.ssl.clientAuth=true+ in +accumulo-site.xml+.
+This requires that each client has access to  valid certificate to set up a 
secure connection
+to the servers. By default, Accumulo uses one-way SSL which does not require 
clients to have
+their own certificate.
+
+=== Client configuration
+
+To establish a connection to Accumulo servers, each client must also have
+special configuration. This is typically accomplished through the use of
+the client configuration file whose default location is +~/.accumulo/config+.
+
+The following properties must be set to connect to an Accumulo instance using 
SSL:
+
+* *rpc.javax.net.ssl.trustStore*=_The path on the local filesystem to the 
keystore containing the certificate authority's public key_
+* *rpc.javax.net.ssl.trustStorePassword*=_The password for the keystore 
containing the certificate authority's public key_
+* *instance.rpc.ssl.enabled*=_true_
+
+If two-way SSL if enabled (+instance.rpc.ssl.clientAuth=true+) for the 
instance, the client must also define
+their own certificate and enable client authenticate as well.
+
+* *rpc.javax.net.ssl.keyStore*=_The path on the local filesystem to the 
keystore containing the server's certificate_
+* *rpc.javax.net.ssl.keyStorePassword*=_The password for the keystore 
containing the server's certificate_
+* *instance.rpc.ssl.clientAuth*=_true_
+
+=== Generating SSL material using OpenSSL
+
+The following is included as an example for generating your own SSL material 
(certificate authority and server/client
+certificates) using OpenSSL and Java's KeyTool command.
+
+==== Generate a certificate authority
+
+----
+# Create a private key
+openssl genrsa -des3 -out root.key 4096
+
+# Create a certificate request using the private key
+openssl req -x509 -new -key root.key -days 365 -out root.pem
+
+# Generate a Base64-encoded version of the PEM just created
+openssl x509 -outform der -in root.pem -out root.der
+
+# Import the key into a Java KeyStore
+keytool -import -alias root-key -keystore truststore.jks -file root.der
+
+# Remove the DER formatted key file (as we don't need it anymore)
+rm root.der
+----
+
+The +truststore.jks+ file is the Java keystore which contains the certificate 
authority's public key.
+
+==== Generate a certificate/keystore per host
+
+It's common that each host in the instance is issued its own certificate 
(notably to ensure that revocation procedures
+can be easily followed). The following steps can be taken for each host.
+
+----
+# Create the private key for our server
+openssl genrsa -out server.key 4096
+
+# Generate a certificate signing request (CSR) with our private key
+openssl req -new -key server.key -out server.csr
+
+# Use the CSR and the CA to create a certificate for the server (a reply to 
the CSR)
+openssl x509 -req -in server.csr -CA root.pem -CAkey root.key -CAcreateserial \
+    -out server.crt -days 365
+
+# Use the certificate and the private key for our server to create PKCS12 file
+openssl pkcs12 -export -in server.crt -inkey server.key -certfile server.crt \
+    -name 'server-key' -out server.p12
+
+# Create a Java KeyStore for the server using the PKCS12 file (private key)
+keytool -importkeystore -srckeystore server.p12 -srcstoretype pkcs12 
-destkeystore \
+    server.jks -deststoretype JKS
+
+# Remove the PKCS12 file as we don't need it
+rm server.p12
+
+# Import the CA-signed certificate to the keystore
+keytool -import -trustcacerts -alias server-crt -file server.crt -keystore 
server.jks
+----
+
+The +server.jks+ file is the Java keystore containing the certificate for a 
given host. The above
+methods are equivalent whether the certficate is generate for an Accumulo 
server or a client.

http://git-wip-us.apache.org/repos/asf/accumulo-website/blob/32ac61d3/docs/master/summaries.md
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+// Licensed to the Apache Software Foundation (ASF) under one or more
+// contributor license agreements.  See the NOTICE file distributed with
+// this work for additional information regarding copyright ownership.
+// The ASF licenses this file to You under the Apache License, Version 2.0
+// (the "License"); you may not use this file except in compliance with
+// the License.  You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+== Summary Statistics
+
+=== Overview
+
+Accumulo has the ability to generate summary statistics about data in a table
+using user defined functions.  Currently these statistics are only generated 
for
+data written to files.  Data recently written to Accumulo that is still in
+memory will not contribute to summary statistics.
+
+This feature can be used to inform a user about what data is in their table.
+Summary statistics can also be used by compaction strategies to make decisions
+about which files to compact.  
+
+Summary data is stored in each file Accumulo produces.  Accumulo can gather
+summary information from across a cluster merging it along the way.  In order
+for this to be fast the, summary information should fit in cache.  There is a
+dedicated cache for summary data on each tserver with a configurable size.  In
+order for summary data to fit in cache, it should probably be small.
+
+For information on writing a custom summarizer see the javadoc for
++org.apache.accumulo.core.client.summary.Summarizer+.  The package
++org.apache.accumulo.core.client.summary.summarizers+ contains summarizer
+implementations that ship with Accumulo and can be configured for use.
+
+=== Inaccuracies
+
+Summary data can be inaccurate when files are missing summary data or when
+files have extra summary data. Files can contain data outside of a tablets
+boundaries. This can happen as result of bulk imported files and tablet splits.
+When this happens, those files could contain extra summary information.
+Accumulo offsets this some by storing summary information for multiple row
+ranges per a file.  However, the ranges are not granular enough to completely
+offset extra data.
+
+Any source of inaccuracies is reported when summary information is requested.
+In the shell examples below this can be seen on the +File Statistics+ line.
+For files missing summary information, the compact command in the shell has a
++--sf-no-summary+ option.  This options compacts files that do not have the
+summary information configured for the table.  The compact command also has the
++--sf-extra-summary+ option which will compact files with extra summary
+information.
+
+=== Configuring
+
+The following tablet server and table properties configure summarization.
+
+* <<appendices/config.txt#_tserver_cache_summary_size>>
+* <<appendices/config.txt#_tserver_summary_partition_threads>>
+* <<appendices/config.txt#_tserver_summary_remote_threads>>
+* <<appendices/config.txt#_tserver_summary_retrieval_threads>>
+* <<appendices/config.txt#TABLE_SUMMARIZER_PREFIX>>
+* <<appendices/config.txt#_table_file_summary_maxsize>>
+
+=== Permissions
+
+Because summary data may be derived from sensitive data, requesting summary 
data
+requires a special permission.  User must have the table permission
++GET_SUMMARIES+ in order to retrieve summary data.
+
+
+=== Bulk import
+
+When generating rfiles to bulk import into Accumulo, those rfiles can contain
+summary data.  To use this feature, look at the javadoc on the
++AccumuloFileOutputFormat.setSummarizers(...)+ method.  Also,
++org.apache.accumulo.core.client.rfile.RFile+ has options for creating RFiles
+with embedded summary data.
+
+=== Examples
+
+This example walks through using summarizers in the Accumulo shell.  Below a
+table is created and some data is inserted to summarize.
+
+ root@uno> createtable summary_test
+ root@uno summary_test> setauths -u root -s PI,GEO,TIME
+ root@uno summary_test> insert 3b503bd name last Doe
+ root@uno summary_test> insert 3b503bd name first John
+ root@uno summary_test> insert 3b503bd contact address "123 Park Ave, NY, NY" 
-l PI&GEO
+ root@uno summary_test> insert 3b503bd date birth "1/11/1942" -l PI&TIME
+ root@uno summary_test> insert 3b503bd date married "5/11/1962" -l PI&TIME
+ root@uno summary_test> insert 3b503bd contact home_phone 1-123-456-7890 -l PI
+ root@uno summary_test> insert d5d18dd contact address "50 Lake Shore Dr, 
Chicago, IL" -l PI&GEO
+ root@uno summary_test> insert d5d18dd name first Jane
+ root@uno summary_test> insert d5d18dd name last Doe
+ root@uno summary_test> insert d5d18dd date birth 8/15/1969 -l PI&TIME
+ root@uno summary_test> scan -s PI,GEO,TIME
+ 3b503bd contact:address [PI&GEO]    123 Park Ave, NY, NY
+ 3b503bd contact:home_phone [PI]    1-123-456-7890
+ 3b503bd date:birth [PI&TIME]    1/11/1942
+ 3b503bd date:married [PI&TIME]    5/11/1962
+ 3b503bd name:first []    John
+ 3b503bd name:last []    Doe
+ d5d18dd contact:address [PI&GEO]    50 Lake Shore Dr, Chicago, IL
+ d5d18dd date:birth [PI&TIME]    8/15/1969
+ d5d18dd name:first []    Jane
+ d5d18dd name:last []    Doe
+
+After inserting the data, summaries are requested below.  No summaries are 
returned.
+
+ root@uno summary_test> summaries
+
+The visibility summarizer is configured below and the table is flushed.
+Flushing the table creates a file creating summary data in the process. The
+summary data returned counts how many times each column visibility occurred.
+The statistics with a +c:+ prefix are visibilities.  The others are generic
+statistics created by the CountingSummarizer that VisibilitySummarizer 
extends. 
+
+ root@uno summary_test> config -t summary_test -s 
table.summarizer.vis=org.apache.accumulo.core.client.summary.summarizers.VisibilitySummarizer
+ root@uno summary_test> summaries
+ root@uno summary_test> flush -w
+ 2017-02-24 19:54:46,090 [shell.Shell] INFO : Flush of table summary_test 
completed.
+ root@uno summary_test> summaries
+  Summarizer         : 
org.apache.accumulo.core.client.summary.summarizers.VisibilitySummarizer vis {}
+  File Statistics    : [total:1, missing:0, extra:0, large:0]
+  Summary Statistics : 
+     c:                                                           = 4
+     c:PI                                                         = 1
+     c:PI&GEO                                                     = 2
+     c:PI&TIME                                                    = 3
+     emitted                                                      = 10
+     seen                                                         = 10
+     tooLong                                                      = 0
+     tooMany                                                      = 0
+
+VisibilitySummarizer has an option +maxCounters+ that determines the max number
+of column visibilites it will track.  Below this option is set and compaction
+is forced to regenerate summary data.  The new summary data only has three
+visibilites and now the +tooMany+ statistic is 4.  This is the number of
+visibilites that were not counted.
+
+ root@uno summary_test> config -t summary_test -s 
table.summarizer.vis.opt.maxCounters=3
+ root@uno summary_test> compact -w
+ 2017-02-24 19:54:46,267 [shell.Shell] INFO : Compacting table ...
+ 2017-02-24 19:54:47,127 [shell.Shell] INFO : Compaction of table summary_test 
completed for given range
+ root@uno summary_test> summaries
+  Summarizer         : 
org.apache.accumulo.core.client.summary.summarizers.VisibilitySummarizer vis 
{maxCounters=3}
+  File Statistics    : [total:1, missing:0, extra:0, large:0]
+  Summary Statistics : 
+     c:PI                                                         = 1
+     c:PI&GEO                                                     = 2
+     c:PI&TIME                                                    = 3
+     emitted                                                      = 10
+     seen                                                         = 10
+     tooLong                                                      = 0
+     tooMany                                                      = 4
+
+Another summarizer is configured below that tracks the number of deletes.  Also
+a compaction strategy that uses this summary data is configured.  The
++TooManyDeletesCompactionStrategy+ will force a compaction of the tablet when
+the ratio of deletes to non-deletes is over 25%.  This threshold is
+configurable.  Below a delete is added and its reflected in the statistics.  In
+this case there is 1 delete and 10 non-deletes, not enough to force a
+compaction of the tablet.
+
+....
+root@uno summary_test> config -t summary_test -s 
table.summarizer.del=org.apache.accumulo.core.client.summary.summarizers.DeletesSummarizer
+root@uno summary_test> compact -w
+2017-02-24 19:54:47,282 [shell.Shell] INFO : Compacting table ...
+2017-02-24 19:54:49,236 [shell.Shell] INFO : Compaction of table summary_test 
completed for given range
+root@uno summary_test> config -t summary_test -s 
table.compaction.major.ratio=10
+root@uno summary_test> config -t summary_test -s 
table.majc.compaction.strategy=org.apache.accumulo.tserver.compaction.strategies.TooManyDeletesCompactionStrategy
+root@uno summary_test> deletemany -r d5d18dd -c date -f
+[DELETED] d5d18dd date:birth [PI&TIME]
+root@uno summary_test> flush -w
+2017-02-24 19:54:49,686 [shell.Shell] INFO : Flush of table summary_test 
completed.
+root@uno summary_test> summaries
+ Summarizer         : 
org.apache.accumulo.core.client.summary.summarizers.VisibilitySummarizer vis 
{maxCounters=3}
+ File Statistics    : [total:2, missing:0, extra:0, large:0]
+ Summary Statistics : 
+    c:PI                                                         = 1
+    c:PI&GEO                                                     = 2
+    c:PI&TIME                                                    = 4
+    emitted                                                      = 11
+    seen                                                         = 11
+    tooLong                                                      = 0
+    tooMany                                                      = 4
+
+ Summarizer         : 
org.apache.accumulo.core.client.summary.summarizers.DeletesSummarizer del {}
+ File Statistics    : [total:2, missing:0, extra:0, large:0]
+ Summary Statistics : 
+    deletes                                                      = 1
+    total                                                        = 11
+....
+
+Some more deletes are added and the table is flushed below.  This results in 4
+deletes and 10 non-deletes, which triggers a full compaction.  A full
+compaction of all files is the only time when delete markers are dropped.  The
+compaction ratio was set to 10 above to show that the number of files did not
+trigger the compaction.   After the compaction there no deletes 6 non-deletes.
+
+....
+root@uno summary_test> deletemany -r d5d18dd -f
+[DELETED] d5d18dd contact:address [PI&GEO]
+[DELETED] d5d18dd name:first []
+[DELETED] d5d18dd name:last []
+root@uno summary_test> flush -w
+2017-02-24 19:54:52,800 [shell.Shell] INFO : Flush of table summary_test 
completed.
+root@uno summary_test> summaries
+ Summarizer         : 
org.apache.accumulo.core.client.summary.summarizers.VisibilitySummarizer vis 
{maxCounters=3}
+ File Statistics    : [total:1, missing:0, extra:0, large:0]
+ Summary Statistics : 
+    c:PI                                                         = 1
+    c:PI&GEO                                                     = 1
+    c:PI&TIME                                                    = 2
+    emitted                                                      = 6
+    seen                                                         = 6
+    tooLong                                                      = 0
+    tooMany                                                      = 2
+
+ Summarizer         : 
org.apache.accumulo.core.client.summary.summarizers.DeletesSummarizer del {}
+ File Statistics    : [total:1, missing:0, extra:0, large:0]
+ Summary Statistics : 
+    deletes                                                      = 0
+    total                                                        = 6
+root@uno summary_test>   
+....
+

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+// Licensed to the Apache Software Foundation (ASF) under one or more
+// contributor license agreements.  See the NOTICE file distributed with
+// this work for additional information regarding copyright ownership.
+// The ASF licenses this file to You under the Apache License, Version 2.0
+// (the "License"); you may not use this file except in compliance with
+// the License.  You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+== Table Configuration
+
+Accumulo tables have a few options that can be configured to alter the default
+behavior of Accumulo as well as improve performance based on the data stored.
+These include locality groups, constraints, bloom filters, iterators, and block
+cache.  For a complete list of available configuration options, see 
<<configuration>>.
+
+=== Locality Groups
+
+Accumulo supports storing sets of column families separately on disk to allow
+clients to efficiently scan over columns that are frequently used together and 
to avoid
+scanning over column families that are not requested. After a locality group 
is set,
+Scanner and BatchScanner operations will automatically take advantage of them
+whenever the fetchColumnFamilies() method is used.
+
+By default, tables place all column families into the same ``default'' 
locality group.
+Additional locality groups can be configured at any time via the shell or
+programmatically as follows:
+
+==== Managing Locality Groups via the Shell
+
+  usage: setgroups <group>=<col fam>{,<col fam>}{ <group>=<col fam>{,<col 
fam>}}
+      [-?] -t <table>
+
+  user@myinstance mytable> setgroups group_one=colf1,colf2 -t mytable
+
+  user@myinstance mytable> getgroups -t mytable
+
+==== Managing Locality Groups via the Client API
+
+[source,java]
+----
+Connector conn;
+
+HashMap<String,Set<Text>> localityGroups = new HashMap<String, Set<Text>>();
+
+HashSet<Text> metadataColumns = new HashSet<Text>();
+metadataColumns.add(new Text("domain"));
+metadataColumns.add(new Text("link"));
+
+HashSet<Text> contentColumns = new HashSet<Text>();
+contentColumns.add(new Text("body"));
+contentColumns.add(new Text("images"));
+
+localityGroups.put("metadata", metadataColumns);
+localityGroups.put("content", contentColumns);
+
+conn.tableOperations().setLocalityGroups("mytable", localityGroups);
+
+// existing locality groups can be obtained as follows
+Map<String, Set<Text>> groups =
+    conn.tableOperations().getLocalityGroups("mytable");
+----
+
+The assignment of Column Families to Locality Groups can be changed at any 
time. The
+physical movement of column families into their new locality groups takes 
place via
+the periodic Major Compaction process that takes place continuously in the
+background. Major Compaction can also be scheduled to take place immediately
+through the shell:
+
+  user@myinstance mytable> compact -t mytable
+
+=== Constraints
+
+Accumulo supports constraints applied on mutations at insert time. This can be
+used to disallow certain inserts according to a user defined policy. Any 
mutation
+that fails to meet the requirements of the constraint is rejected and sent 
back to the
+client.
+
+Constraints can be enabled by setting a table property as follows:
+
+----
+user@myinstance mytable> constraint -t mytable -a com.test.ExampleConstraint 
com.test.AnotherConstraint
+
+user@myinstance mytable> constraint -l
+com.test.ExampleConstraint=1
+com.test.AnotherConstraint=2
+----
+
+Currently there are no general-purpose constraints provided with the Accumulo
+distribution. New constraints can be created by writing a Java class that 
implements
+the following interface:
+
+  org.apache.accumulo.core.constraints.Constraint
+
+To deploy a new constraint, create a jar file containing the class 
implementing the
+new constraint and place it in the lib directory of the Accumulo installation. 
New
+constraint jars can be added to Accumulo and enabled without restarting but any
+change to an existing constraint class requires Accumulo to be restarted.
+
+See the 
https://github.com/apache/accumulo-examples/blob/master/docs/contraints.md[constraints
 example]
+for example code.
+
+=== Bloom Filters
+
+As mutations are applied to an Accumulo table, several files are created per 
tablet. If
+bloom filters are enabled, Accumulo will create and load a small data 
structure into
+memory to determine whether a file contains a given key before opening the 
file.
+This can speed up lookups considerably.
+
+To enable bloom filters, enter the following command in the Shell:
+
+  user@myinstance> config -t mytable -s table.bloom.enabled=true
+
+The 
https://github.com/apache/accumulo-examples/blob/master/docs/bloom.md[bloom 
filter example]
+contains an extensive example of using Bloom Filters.
+
+=== Iterators
+
+Iterators provide a modular mechanism for adding functionality to be executed 
by
+TabletServers when scanning or compacting data. This allows users to 
efficiently
+summarize, filter, and aggregate data. In fact, the built-in features of 
cell-level
+security and column fetching are implemented using Iterators.
+Some useful Iterators are provided with Accumulo and can be found in the
+*+org.apache.accumulo.core.iterators.user+* package.
+In each case, any custom Iterators must be included in Accumulo's classpath,
+typically by including a jar in +lib/+ or +lib/ext/+, although the VFS 
classloader
+allows for classpath manipulation using a variety of schemes including URLs 
and HDFS URIs.
+
+==== Setting Iterators via the Shell
+
+Iterators can be configured on a table at scan, minor compaction and/or major
+compaction scopes. If the Iterator implements the OptionDescriber interface, 
the
+setiter command can be used which will interactively prompt the user to provide
+values for the given necessary options.
+
+  usage: setiter [-?] -ageoff | -agg | -class <name> | -regex |
+      -reqvis | -vers   [-majc] [-minc] [-n <itername>] -p <pri>
+      [-scan] [-t <table>]
+
+  user@myinstance mytable> setiter -t mytable -scan -p 15 -n myiter -class 
com.company.MyIterator
+
+The config command can always be used to manually configure iterators which is 
useful
+in cases where the Iterator does not implement the OptionDescriber interface.
+
+  config -t mytable -s table.iterator.scan.myiter=15,com.company.MyIterator
+  config -t mytable -s table.iterator.minc.myiter=15,com.company.MyIterator
+  config -t mytable -s table.iterator.majc.myiter=15,com.company.MyIterator
+  config -t mytable -s 
table.iterator.scan.myiter.opt.myoptionname=myoptionvalue
+  config -t mytable -s 
table.iterator.minc.myiter.opt.myoptionname=myoptionvalue
+  config -t mytable -s 
table.iterator.majc.myiter.opt.myoptionname=myoptionvalue
+
+Typically, a table will have multiple iterators. Accumulo configures a set of
+system level iterators for each table. These iterators provide core
+functionality like visibility label filtering and may not be removed by
+users. User level iterators are applied in the order of their priority.
+Priority is a user configured integer; iterators with lower numbers go first,
+passing the results of their iteration on to the other iterators up the
+stack.
+
+==== Setting Iterators Programmatically
+
+[source,java]
+scanner.addIterator(new IteratorSetting(
+    15, // priority
+    "myiter", // name this iterator
+    "com.company.MyIterator" // class name
+));
+
+Some iterators take additional parameters from client code, as in the following
+example:
+
+[source,java]
+IteratorSetting iter = new IteratorSetting(...);
+iter.addOption("myoptionname", "myoptionvalue");
+scanner.addIterator(iter)
+
+Tables support separate Iterator settings to be applied at scan time, upon 
minor
+compaction and upon major compaction. For most uses, tables will have identical
+iterator settings for all three to avoid inconsistent results.
+
+==== Versioning Iterators and Timestamps
+
+Accumulo provides the capability to manage versioned data through the use of
+timestamps within the Key. If a timestamp is not specified in the key created 
by the
+client then the system will set the timestamp to the current time. Two keys 
with
+identical rowIDs and columns but different timestamps are considered two 
versions
+of the same key. If two inserts are made into Accumulo with the same rowID,
+column, and timestamp, then the behavior is non-deterministic.
+
+Timestamps are sorted in descending order, so the most recent data comes first.
+Accumulo can be configured to return the top k versions, or versions later 
than a
+given date. The default is to return the one most recent version.
+
+The version policy can be changed by changing the VersioningIterator options 
for a
+table as follows:
+
+----
+user@myinstance mytable> config -t mytable -s 
table.iterator.scan.vers.opt.maxVersions=3
+
+user@myinstance mytable> config -t mytable -s 
table.iterator.minc.vers.opt.maxVersions=3
+
+user@myinstance mytable> config -t mytable -s 
table.iterator.majc.vers.opt.maxVersions=3
+----
+
+When a table is created, by default its configured to use the
+VersioningIterator and keep one version. A table can be created without the
+VersioningIterator with the -ndi option in the shell. Also the Java API
+has the following method
+
+[source,java]
+connector.tableOperations.create(String tableName, boolean limitVersion);
+
+===== Logical Time
+
+Accumulo 1.2 introduces the concept of logical time. This ensures that 
timestamps
+set by Accumulo always move forward. This helps avoid problems caused by
+TabletServers that have different time settings. The per tablet counter gives 
unique
+one up time stamps on a per mutation basis. When using time in milliseconds, if
+two things arrive within the same millisecond then both receive the same
+timestamp. When using time in milliseconds, Accumulo set times will still
+always move forward and never backwards.
+
+A table can be configured to use logical timestamps at creation time as 
follows:
+
+  user@myinstance> createtable -tl logical
+
+===== Deletes
+
+Deletes are special keys in Accumulo that get sorted along will all the other 
data.
+When a delete key is inserted, Accumulo will not show anything that has a
+timestamp less than or equal to the delete key. During major compaction, any 
keys
+older than a delete key are omitted from the new file created, and the omitted 
keys
+are removed from disk as part of the regular garbage collection process.
+
+==== Filters
+
+When scanning over a set of key-value pairs it is possible to apply an 
arbitrary
+filtering policy through the use of a Filter. Filters are types of iterators 
that return
+only key-value pairs that satisfy the filter logic. Accumulo has a few 
built-in filters
+that can be configured on any table: AgeOff, ColumnAgeOff, Timestamp, NoVis, 
and RegEx. More can be added
+by writing a Java class that extends the
++org.apache.accumulo.core.iterators.Filter+ class.
+
+The AgeOff filter can be configured to remove data older than a certain date 
or a fixed
+amount of time from the present. The following example sets a table to delete
+everything inserted over 30 seconds ago:
+
+----
+user@myinstance> createtable filtertest
+
+user@myinstance filtertest> setiter -t filtertest -scan -minc -majc -p 10 -n 
myfilter -ageoff
+AgeOffFilter removes entries with timestamps more than <ttl> milliseconds old
+----------> set org.apache.accumulo.core.iterators.user.AgeOffFilter parameter 
negate, default false
+                keeps k/v that pass accept method, true rejects k/v that pass 
accept method:
+----------> set org.apache.accumulo.core.iterators.user.AgeOffFilter parameter 
ttl, time to
+                live (milliseconds): 30000
+----------> set org.apache.accumulo.core.iterators.user.AgeOffFilter parameter 
currentTime, if set,
+                use the given value as the absolute time in milliseconds as 
the current time of day:
+
+user@myinstance filtertest>
+
+user@myinstance filtertest> scan
+
+user@myinstance filtertest> insert foo a b c
+
+user@myinstance filtertest> scan
+foo a:b [] c
+
+user@myinstance filtertest> sleep 4
+
+user@myinstance filtertest> scan
+
+user@myinstance filtertest>
+----
+
+To see the iterator settings for a table, use:
+
+  user@example filtertest> config -t filtertest -f iterator
+  ---------+---------------------------------------------+------------------
+  SCOPE    | NAME                                        | VALUE
+  ---------+---------------------------------------------+------------------
+  table    | table.iterator.majc.myfilter .............. | 
10,org.apache.accumulo.core.iterators.user.AgeOffFilter
+  table    | table.iterator.majc.myfilter.opt.ttl ...... | 30000
+  table    | table.iterator.majc.vers .................. | 
20,org.apache.accumulo.core.iterators.VersioningIterator
+  table    | table.iterator.majc.vers.opt.maxVersions .. | 1
+  table    | table.iterator.minc.myfilter .............. | 
10,org.apache.accumulo.core.iterators.user.AgeOffFilter
+  table    | table.iterator.minc.myfilter.opt.ttl ...... | 30000
+  table    | table.iterator.minc.vers .................. | 
20,org.apache.accumulo.core.iterators.VersioningIterator
+  table    | table.iterator.minc.vers.opt.maxVersions .. | 1
+  table    | table.iterator.scan.myfilter .............. | 
10,org.apache.accumulo.core.iterators.user.AgeOffFilter
+  table    | table.iterator.scan.myfilter.opt.ttl ...... | 30000
+  table    | table.iterator.scan.vers .................. | 
20,org.apache.accumulo.core.iterators.VersioningIterator
+  table    | table.iterator.scan.vers.opt.maxVersions .. | 1
+  ---------+---------------------------------------------+------------------
+
+==== Combiners
+
+Accumulo supports on the fly lazy aggregation of data using Combiners. 
Aggregation is
+done at compaction and scan time. No lookup is done at insert time, which` 
greatly
+speeds up ingest.
+
+Accumulo allows Combiners to be configured on tables and column
+families. When a Combiner is set it is applied across the values
+associated with any keys that share rowID, column family, and column qualifier.
+This is similar to the reduce step in MapReduce, which applied some function 
to all
+the values associated with a particular key.
+
+For example, if a summing combiner were configured on a table and the following
+mutations were inserted:
+
+  Row     Family Qualifier Timestamp  Value
+  rowID1  colfA  colqA     20100101   1
+  rowID1  colfA  colqA     20100102   1
+
+The table would reflect only one aggregate value:
+
+  rowID1  colfA  colqA     -          2
+
+Combiners can be enabled for a table using the setiter command in the shell. 
Below is an example.
+
+----
+root@a14 perDayCounts> setiter -t perDayCounts -p 10 -scan -minc -majc -n 
daycount
+                       -class 
org.apache.accumulo.core.iterators.user.SummingCombiner
+TypedValueCombiner can interpret Values as a variety of number encodings
+  (VLong, Long, or String) before combining
+----------> set SummingCombiner parameter columns,
+            <col fam>[:<col qual>]{,<col fam>[:<col qual>]} : day
+----------> set SummingCombiner parameter type, <VARNUM|LONG|STRING>: STRING
+
+root@a14 perDayCounts> insert foo day 20080101 1
+root@a14 perDayCounts> insert foo day 20080101 1
+root@a14 perDayCounts> insert foo day 20080103 1
+root@a14 perDayCounts> insert bar day 20080101 1
+root@a14 perDayCounts> insert bar day 20080101 1
+
+root@a14 perDayCounts> scan
+bar day:20080101 []    2
+foo day:20080101 []    2
+foo day:20080103 []    1
+----
+
+Accumulo includes some useful Combiners out of the box. To find these look in
+the *+org.apache.accumulo.core.iterators.user+* package.
+
+Additional Combiners can be added by creating a Java class that extends
++org.apache.accumulo.core.iterators.Combiner+ and adding a jar containing that
+class to Accumulo's lib/ext directory.
+
+See the 
https://github.com/apache/accumulo-examples/blob/master/docs/combiner.md[combiner
 example]
+for example code.
+
+=== Block Cache
+
+In order to increase throughput of commonly accessed entries, Accumulo employs 
a block cache.
+This block cache buffers data in memory so that it doesn't have to be read off 
of disk.
+The RFile format that Accumulo prefers is a mix of index blocks and data 
blocks, where the index blocks are used to find the appropriate data blocks.
+Typical queries to Accumulo result in a binary search over several index 
blocks followed by a linear scan of one or more data blocks.
+
+The block cache can be configured on a per-table basis, and all tablets hosted 
on a tablet server share a single resource pool.
+To configure the size of the tablet server's block cache, set the following 
properties:
+
+  tserver.cache.data.size: Specifies the size of the cache for file data 
blocks.
+  tserver.cache.index.size: Specifies the size of the cache for file indices.
+
+To enable the block cache for your table, set the following properties:
+
+  table.cache.block.enable: Determines whether file (data) block cache is 
enabled.
+  table.cache.index.enable: Determines whether index cache is enabled.
+
+The block cache can have a significant effect on alleviating hot spots, as 
well as reducing query latency.
+It is enabled by default for the metadata tables.
+
+=== Compaction
+
+As data is written to Accumulo it is buffered in memory. The data buffered in
+memory is eventually written to HDFS on a per tablet basis. Files can also be
+added to tablets directly by bulk import. In the background tablet servers run
+major compactions to merge multiple files into one. The tablet server has to
+decide which tablets to compact and which files within a tablet to compact.
+This decision is made using the compaction ratio, which is configurable on a
+per table basis. To configure this ratio modify the following property:
+
+  table.compaction.major.ratio
+
+Increasing this ratio will result in more files per tablet and less compaction
+work. More files per tablet means more higher query latency. So adjusting
+this ratio is a trade off between ingest and query performance. The ratio
+defaults to 3.
+
+The way the ratio works is that a set of files is compacted into one file if 
the
+sum of the sizes of the files in the set is larger than the ratio multiplied by
+the size of the largest file in the set. If this is not true for the set of all
+files in a tablet, the largest file is removed from consideration, and the
+remaining files are considered for compaction. This is repeated until a
+compaction is triggered or there are no files left to consider.
+
+The number of background threads tablet servers use to run major compactions is
+configurable. To configure this modify the following property:
+
+  tserver.compaction.major.concurrent.max
+
+Also, the number of threads tablet servers use for minor compactions is
+configurable. To configure this modify the following property:
+
+  tserver.compaction.minor.concurrent.max
+
+The numbers of minor and major compactions running and queued is visible on the
+Accumulo monitor page. This allows you to see if compactions are backing up
+and adjustments to the above settings are needed. When adjusting the number of
+threads available for compactions, consider the number of cores and other tasks
+running on the nodes such as maps and reduces.
+
+If major compactions are not keeping up, then the number of files per tablet
+will grow to a point such that query performance starts to suffer. One way to
+handle this situation is to increase the compaction ratio. For example, if the
+compaction ratio were set to 1, then every new file added to a tablet by minor
+compaction would immediately queue the tablet for major compaction. So if a
+tablet has a 200M file and minor compaction writes a 1M file, then the major
+compaction will attempt to merge the 200M and 1M file. If the tablet server
+has lots of tablets trying to do this sort of thing, then major compactions
+will back up and the number of files per tablet will start to grow, assuming
+data is being continuously written. Increasing the compaction ratio will
+alleviate backups by lowering the amount of major compaction work that needs to
+be done.
+
+Another option to deal with the files per tablet growing too large is to adjust
+the following property:
+
+  table.file.max
+
+When a tablet reaches this number of files and needs to flush its in-memory
+data to disk, it will choose to do a merging minor compaction. A merging minor
+compaction will merge the tablet's smallest file with the data in memory at
+minor compaction time. Therefore the number of files will not grow beyond this
+limit. This will make minor compactions take longer, which will cause ingest
+performance to decrease. This can cause ingest to slow down until major
+compactions have enough time to catch up. When adjusting this property, also
+consider adjusting the compaction ratio. Ideally, merging minor compactions
+never need to occur and major compactions will keep up. It is possible to
+configure the file max and compaction ratio such that only merging minor
+compactions occur and major compactions never occur. This should be avoided
+because doing only merging minor compactions causes O(_N_^2^) work to be done.
+The amount of work done by major compactions is O(_N_*log~_R_~(_N_)) where
+_R_ is the compaction ratio.
+
+Compactions can be initiated manually for a table. To initiate a minor
+compaction, use the flush command in the shell. To initiate a major compaction,
+use the compact command in the shell. The compact command will compact all
+tablets in a table to one file. Even tablets with one file are compacted. This
+is useful for the case where a major compaction filter is configured for a
+table. In 1.4 the ability to compact a range of a table was added. To use this
+feature specify start and stop rows for the compact command. This will only
+compact tablets that overlap the given row range.
+
+==== Compaction Strategies
+
+The default behavior of major compactions is defined in the class 
DefaultCompactionStrategy. 
+This behavior can be changed by overriding the following property with a fully 
qualified class name:
+
+  table.majc.compaction.strategy
+
+Custom compaction strategies can have additional properties that are specified 
following the prefix property:
+
+  table.majc.compaction.strategy.opts.*
+
+Accumulo provides a few classes that can be used as an alternative compaction 
strategy. These classes are located in the 
+org.apache.accumulo.tserver.compaction.* package. EverythingCompactionStrategy 
will simply compact all files. This is the 
+strategy used by the user "compact" command. SizeLimitCompactionStrategy 
compacts files no bigger than the limit set in the
+property table.majc.compaction.strategy.opts.sizeLimit. 
+
+TwoTierCompactionStrategy is a hybrid compaction strategy that supports two 
types of compression. If the total size of 
+files being compacted is larger than 
table.majc.compaction.strategy.opts.file.large.compress.threshold than a larger 
+compression type will be used. The larger compression type is specified in 
table.majc.compaction.strategy.opts.file.large.compress.type. 
+Otherwise, the configured table compression will be used. To use this strategy 
with minor compactions set table.file.compress.type=snappy 
+and set a different compress type in 
table.majc.compaction.strategy.opts.file.large.compress.type for larger files.
+
+=== Pre-splitting tables
+
+Accumulo will balance and distribute tables across servers. Before a
+table gets large, it will be maintained as a single tablet on a single
+server. This limits the speed at which data can be added or queried
+to the speed of a single node. To improve performance when the a table
+is new, or small, you can add split points and generate new tablets.
+
+In the shell:
+
+  root@myinstance> createtable newTable
+  root@myinstance> addsplits -t newTable g n t
+
+This will create a new table with 4 tablets. The table will be split
+on the letters ``g'', ``n'', and ``t'' which will work nicely if the
+row data start with lower-case alphabetic characters. If your row
+data includes binary information or numeric information, or if the
+distribution of the row information is not flat, then you would pick
+different split points. Now ingest and query can proceed on 4 nodes
+which can improve performance.
+
+=== Merging tablets
+
+Over time, a table can get very large, so large that it has hundreds
+of thousands of split points. Once there are enough tablets to spread
+a table across the entire cluster, additional splits may not improve
+performance, and may create unnecessary bookkeeping. The distribution
+of data may change over time. For example, if row data contains date
+information, and data is continually added and removed to maintain a
+window of current information, tablets for older rows may be empty.
+
+Accumulo supports tablet merging, which can be used to reduce
+the number of split points. The following command will merge all rows
+from ``A'' to ``Z'' into a single tablet:
+
+  root@myinstance> merge -t myTable -s A -e Z
+
+If the result of a merge produces a tablet that is larger than the
+configured split size, the tablet may be split by the tablet server.
+Be sure to increase your tablet size prior to any merges if the goal
+is to have larger tablets:
+
+  root@myinstance> config -t myTable -s table.split.threshold=2G
+
+In order to merge small tablets, you can ask Accumulo to merge
+sections of a table smaller than a given size.
+
+  root@myinstance> merge -t myTable -s 100M
+
+By default, small tablets will not be merged into tablets that are
+already larger than the given size. This can leave isolated small
+tablets. To force small tablets to be merged into larger tablets use
+the +--force+ option:
+
+  root@myinstance> merge -t myTable -s 100M --force
+
+Merging away small tablets works on one section at a time. If your
+table contains many sections of small split points, or you are
+attempting to change the split size of the entire table, it will be
+faster to set the split point and merge the entire table:
+
+  root@myinstance> config -t myTable -s table.split.threshold=256M
+  root@myinstance> merge -t myTable
+
+=== Delete Range
+
+Consider an indexing scheme that uses date information in each row.
+For example ``20110823-15:20:25.013'' might be a row that specifies a
+date and time. In some cases, we might like to delete rows based on
+this date, say to remove all the data older than the current year.
+Accumulo supports a delete range operation which efficiently
+removes data between two rows. For example:
+
+  root@myinstance> deleterange -t myTable -s 2010 -e 2011
+
+This will delete all rows starting with ``2010'' and it will stop at
+any row starting ``2011''. You can delete any data prior to 2011
+with:
+
+  root@myinstance> deleterange -t myTable -e 2011 --force
+
+The shell will not allow you to delete an unbounded range (no start)
+unless you provide the +--force+ option.
+
+Range deletion is implemented using splits at the given start/end
+positions, and will affect the number of splits in the table.
+
+=== Cloning Tables
+
+A new table can be created that points to an existing table's data. This is a
+very quick metadata operation, no data is actually copied. The cloned table
+and the source table can change independently after the clone operation. One
+use case for this feature is testing. For example to test a new filtering
+iterator, clone the table, add the filter to the clone, and force a major
+compaction. To perform a test on less data, clone a table and then use delete
+range to efficiently remove a lot of data from the clone. Another use case is
+generating a snapshot to guard against human error. To create a snapshot,
+clone a table and then disable write permissions on the clone.
+
+The clone operation will point to the source table's files. This is why the
+flush option is present and is enabled by default in the shell. If the flush
+option is not enabled, then any data the source table currently has in memory
+will not exist in the clone.
+
+A cloned table copies the configuration of the source table. However the
+permissions of the source table are not copied to the clone. After a clone is
+created, only the user that created the clone can read and write to it.
+
+In the following example we see that data inserted after the clone operation is
+not visible in the clone.
+
+----
+root@a14> createtable people
+
+root@a14 people> insert 890435 name last Doe
+root@a14 people> insert 890435 name first John
+
+root@a14 people> clonetable people test
+
+root@a14 people> insert 890436 name first Jane
+root@a14 people> insert 890436 name last Doe
+
+root@a14 people> scan
+890435 name:first []    John
+890435 name:last []    Doe
+890436 name:first []    Jane
+890436 name:last []    Doe
+
+root@a14 people> table test
+
+root@a14 test> scan
+890435 name:first []    John
+890435 name:last []    Doe
+
+root@a14 test>
+----
+
+The du command in the shell shows how much space a table is using in HDFS.
+This command can also show how much overlapping space two cloned tables have in
+HDFS. In the example below du shows table ci is using 428M. Then ci is cloned
+to cic and du shows that both tables share 428M. After three entries are
+inserted into cic and its flushed, du shows the two tables still share 428M but
+cic has 226 bytes to itself. Finally, table cic is compacted and then du shows
+that each table uses 428M.
+
+----
+root@a14> du ci
+             428,482,573 [ci]
+
+root@a14> clonetable ci cic
+
+root@a14> du ci cic
+             428,482,573 [ci, cic]
+
+root@a14> table cic
+
+root@a14 cic> insert r1 cf1 cq1 v1
+root@a14 cic> insert r1 cf1 cq2 v2
+root@a14 cic> insert r1 cf1 cq3 v3
+
+root@a14 cic> flush -t cic -w
+27 15:00:13,908 [shell.Shell] INFO : Flush of table cic completed.
+
+root@a14 cic> du ci cic
+             428,482,573 [ci, cic]
+                     226 [cic]
+
+root@a14 cic> compact -t cic -w
+27 15:00:35,871 [shell.Shell] INFO : Compacting table ...
+27 15:03:03,303 [shell.Shell] INFO : Compaction of table cic completed for 
given range
+
+root@a14 cic> du ci cic
+             428,482,573 [ci]
+             428,482,612 [cic]
+
+root@a14 cic>
+----
+
+=== Exporting Tables
+
+Accumulo supports exporting tables for the purpose of copying tables to another
+cluster. Exporting and importing tables preserves the tables configuration,
+splits, and logical time. Tables are exported and then copied via the hadoop
+distcp command. To export a table, it must be offline and stay offline while
+discp runs. The reason it needs to stay offline is to prevent files from being
+deleted. A table can be cloned and the clone taken offline inorder to avoid
+losing access to the table. See the 
https://github.com/apache/accumulo-examples/blob/master/docs/export.md[export 
example]
+for example code.
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/accumulo-website/blob/32ac61d3/docs/master/table_design.md
----------------------------------------------------------------------
diff --git a/docs/master/table_design.md b/docs/master/table_design.md
new file mode 100644
index 0000000..31fa49a
--- /dev/null
+++ b/docs/master/table_design.md
@@ -0,0 +1,336 @@
+// Licensed to the Apache Software Foundation (ASF) under one or more
+// contributor license agreements.  See the NOTICE file distributed with
+// this work for additional information regarding copyright ownership.
+// The ASF licenses this file to You under the Apache License, Version 2.0
+// (the "License"); you may not use this file except in compliance with
+// the License.  You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+== Table Design
+
+=== Basic Table
+
+Since Accumulo tables are sorted by row ID, each table can be thought of as 
being
+indexed by the row ID. Lookups performed by row ID can be executed quickly, by 
doing
+a binary search, first across the tablets, and then within a tablet. Clients 
should
+choose a row ID carefully in order to support their desired application. A 
simple rule
+is to select a unique identifier as the row ID for each entity to be stored 
and assign
+all the other attributes to be tracked to be columns under this row ID. For 
example,
+if we have the following data in a comma-separated file:
+
+  userid,age,address,account-balance
+
+We might choose to store this data using the userid as the rowID, the column
+name in the column family, and a blank column qualifier:
+
+[source,java]
+----
+Mutation m = new Mutation(userid);
+final String column_qualifier = "";
+m.put("age", column_qualifier, age);
+m.put("address", column_qualifier, address);
+m.put("balance", column_qualifier, account_balance);
+
+writer.add(m);
+----
+
+We could then retrieve any of the columns for a specific userid by specifying 
the
+userid as the range of a scanner and fetching specific columns:
+
+[source,java]
+----
+Range r = new Range(userid, userid); // single row
+Scanner s = conn.createScanner("userdata", auths);
+s.setRange(r);
+s.fetchColumnFamily(new Text("age"));
+
+for(Entry<Key,Value> entry : s) {
+  System.out.println(entry.getValue().toString());
+}
+----
+
+=== RowID Design
+
+Often it is necessary to transform the rowID in order to have rows ordered in 
a way
+that is optimal for anticipated access patterns. A good example of this is 
reversing
+the order of components of internet domain names in order to group rows of the
+same parent domain together:
+
+  com.google.code
+  com.google.labs
+  com.google.mail
+  com.yahoo.mail
+  com.yahoo.research
+
+Some data may result in the creation of very large rows - rows with many 
columns.
+In this case the table designer may wish to split up these rows for better load
+balancing while keeping them sorted together for scanning purposes. This can be
+done by appending a random substring at the end of the row:
+
+  com.google.code_00
+  com.google.code_01
+  com.google.code_02
+  com.google.labs_00
+  com.google.mail_00
+  com.google.mail_01
+
+It could also be done by adding a string representation of some period of time 
such as date to the week
+or month:
+
+  com.google.code_201003
+  com.google.code_201004
+  com.google.code_201005
+  com.google.labs_201003
+  com.google.mail_201003
+  com.google.mail_201004
+
+Appending dates provides the additional capability of restricting a scan to a 
given
+date range.
+
+=== Lexicoders
+Since Keys in Accumulo are sorted lexicographically by default, it's often 
useful to encode
+common data types into a byte format in which their sort order corresponds to 
the sort order
+in their native form. An example of this is encoding dates and numerical data 
so that they can
+be better seeked or searched in ranges.
+
+The lexicoders are a standard and extensible way of encoding Java types. 
Here's an example
+of a lexicoder that encodes a java Date object so that it sorts 
lexicographically:
+
+[source,java]
+----
+// create new date lexicoder
+DateLexicoder dateEncoder = new DateLexicoder();
+
+// truncate time to hours
+long epoch = System.currentTimeMillis();
+Date hour = new Date(epoch - (epoch % 3600000));
+
+// encode the rowId so that it is sorted lexicographically
+Mutation mutation = new Mutation(dateEncoder.encode(hour));
+mutation.put(new Text("colf"), new Text("colq"), new Value(new byte[]{}));
+----
+
+If we want to return the most recent date first, we can reverse the sort order
+with the reverse lexicoder:
+
+[source,java]
+----
+// create new date lexicoder and reverse lexicoder
+DateLexicoder dateEncoder = new DateLexicoder();
+ReverseLexicoder reverseEncoder = new ReverseLexicoder(dateEncoder);
+
+// truncate date to hours
+long epoch = System.currentTimeMillis();
+Date hour = new Date(epoch - (epoch % 3600000));
+
+// encode the rowId so that it sorts in reverse lexicographic order
+Mutation mutation = new Mutation(reverseEncoder.encode(hour));
+mutation.put(new Text("colf"), new Text("colq"), new Value(new byte[]{}));
+----
+
+
+=== Indexing
+In order to support lookups via more than one attribute of an entity, 
additional
+indexes can be built. However, because Accumulo tables can support any number 
of
+columns without specifying them beforehand, a single additional index will 
often
+suffice for supporting lookups of records in the main table. Here, the index 
has, as
+the rowID, the Value or Term from the main table, the column families are the 
same,
+and the column qualifier of the index table contains the rowID from the main 
table.
+
+[width="75%",cols="^,^,^,^"]
+[grid="rows"]
+[options="header"]
+|=============================================
+|RowID |Column Family |Column Qualifier |Value
+|Term  |Field Name    |MainRowID        |
+|=============================================
+
+Note: We store rowIDs in the column qualifier rather than the Value so that we 
can
+have more than one rowID associated with a particular term within the index. 
If we
+stored this in the Value we would only see one of the rows in which the value
+appears since Accumulo is configured by default to return the one most recent
+value associated with a key.
+
+Lookups can then be done by scanning the Index Table first for occurrences of 
the
+desired values in the columns specified, which returns a list of row ID from 
the main
+table. These can then be used to retrieve each matching record, in their 
entirety, or a
+subset of their columns, from the Main Table.
+
+To support efficient lookups of multiple rowIDs from the same table, the 
Accumulo
+client library provides a BatchScanner. Users specify a set of Ranges to the
+BatchScanner, which performs the lookups in multiple threads to multiple 
servers
+and returns an Iterator over all the rows retrieved. The rows returned are NOT 
in
+sorted order, as is the case with the basic Scanner interface.
+
+[source,java]
+----
+// first we scan the index for IDs of rows matching our query
+Text term = new Text("mySearchTerm");
+
+HashSet<Range> matchingRows = new HashSet<Range>();
+
+Scanner indexScanner = createScanner("index", auths);
+indexScanner.setRange(new Range(term, term));
+
+// we retrieve the matching rowIDs and create a set of ranges
+for(Entry<Key,Value> entry : indexScanner) {
+    matchingRows.add(new Range(entry.getKey().getColumnQualifier()));
+}
+
+// now we pass the set of rowIDs to the batch scanner to retrieve them
+BatchScanner bscan = conn.createBatchScanner("table", auths, 10);
+bscan.setRanges(matchingRows);
+bscan.fetchColumnFamily(new Text("attributes"));
+
+for(Entry<Key,Value> entry : bscan) {
+    System.out.println(entry.getValue());
+}
+----
+
+One advantage of the dynamic schema capabilities of Accumulo is that different
+fields may be indexed into the same physical table. However, it may be 
necessary to
+create different index tables if the terms must be formatted differently in 
order to
+maintain proper sort order. For example, real numbers must be formatted
+differently than their usual notation in order to be sorted correctly. In 
these cases,
+usually one index per unique data type will suffice.
+
+=== Entity-Attribute and Graph Tables
+
+Accumulo is ideal for storing entities and their attributes, especially of the
+attributes are sparse. It is often useful to join several datasets together on 
common
+entities within the same table. This can allow for the representation of 
graphs,
+including nodes, their attributes, and connections to other nodes.
+
+Rather than storing individual events, Entity-Attribute or Graph tables store
+aggregate information about the entities involved in the events and the
+relationships between entities. This is often preferrable when single events 
aren't
+very useful and when a continuously updated summarization is desired.
+
+The physical schema for an entity-attribute or graph table is as follows:
+
+[width="75%",cols="^,^,^,^"]
+[grid="rows"]
+[options="header"]
+|==================================================
+|RowID    |Column Family  |Column Qualifier |Value
+|EntityID |Attribute Name |Attribute Value  |Weight
+|EntityID |Edge Type      |Related EntityID |Weight
+|==================================================
+
+For example, to keep track of employees, managers and products the following
+entity-attribute table could be used. Note that the weights are not always 
necessary
+and are set to 0 when not used.
+
+[width="75%",cols="^,^,^,^"]
+[grid="rows"]
+[options="header"]
+|=============================================
+|RowID |Column Family |Column Qualifier |Value
+| E001 | name         | bob             | 0
+| E001 | department   | sales           | 0
+| E001 | hire_date    | 20030102        | 0
+| E001 | units_sold   | P001            | 780
+| E002 | name         | george          | 0
+| E002 | department   | sales           | 0
+| E002 | manager_of   | E001            | 0
+| E002 | manager_of   | E003            | 0
+| E003 | name         | harry           | 0
+| E003 | department   | accounts_recv   | 0
+| E003 | hire_date    | 20000405        | 0
+| E003 | units_sold   | P002            | 566
+| E003 | units_sold   | P001            | 232
+| P001 | product_name | nike_airs       | 0
+| P001 | product_type | shoe            | 0
+| P001 | in_stock     | germany         | 900
+| P001 | in_stock     | brazil          | 200
+| P002 | product_name | basic_jacket    | 0
+| P002 | product_type | clothing        | 0
+| P002 | in_stock     | usa             | 3454
+| P002 | in_stock     | germany         | 700
+|=============================================
+
+To allow efficient updating of edge weights, an aggregating iterator can be
+configured to add the value of all mutations applied with the same key. These 
types
+of tables can easily be created from raw events by simply extracting the 
entities,
+attributes, and relationships from individual events and inserting the keys 
into
+Accumulo each with a count of 1. The aggregating iterator will take care of
+maintaining the edge weights.
+
+=== Document-Partitioned Indexing
+
+Using a simple index as described above works well when looking for records 
that
+match one of a set of given criteria. When looking for records that match more 
than
+one criterion simultaneously, such as when looking for documents that contain 
all of
+the words `the' and `white' and `house', there are several issues.
+
+First is that the set of all records matching any one of the search terms must 
be sent
+to the client, which incurs a lot of network traffic. The second problem is 
that the
+client is responsible for performing set intersection on the sets of records 
returned
+to eliminate all but the records matching all search terms. The memory of the 
client
+may easily be overwhelmed during this operation.
+
+For these reasons Accumulo includes support for a scheme known as sharded
+indexing, in which these set operations can be performed at the TabletServers 
and
+decisions about which records to include in the result set can be made without
+incurring network traffic.
+
+This is accomplished via partitioning records into bins that each reside on at 
most
+one TabletServer, and then creating an index of terms per record within each 
bin as
+follows:
+
+[width="75%",cols="^,^,^,^"]
+[grid="rows"]
+[options="header"]
+|==============================================
+|RowID |Column Family |Column Qualifier |Value
+|BinID |Term          |DocID            |Weight
+|==============================================
+
+Documents or records are mapped into bins by a user-defined ingest 
application. By
+storing the BinID as the RowID we ensure that all the information for a 
particular
+bin is contained in a single tablet and hosted on a single TabletServer since
+Accumulo never splits rows across tablets. Storing the Terms as column families
+serves to enable fast lookups of all the documents within this bin that 
contain the
+given term.
+
+Finally, we perform set intersection operations on the TabletServer via a 
special
+iterator called the Intersecting Iterator. Since documents are partitioned 
into many
+bins, a search of all documents must search every bin. We can use the 
BatchScanner
+to scan all bins in parallel. The Intersecting Iterator should be enabled on a
+BatchScanner within user query code as follows:
+
+[source,java]
+----
+Text[] terms = {new Text("the"), new Text("white"), new Text("house")};
+
+BatchScanner bscan = conn.createBatchScanner(table, auths, 20);
+
+IteratorSetting iter = new IteratorSetting(20, "ii", 
IntersectingIterator.class);
+IntersectingIterator.setColumnFamilies(iter, terms);
+
+bscan.addScanIterator(iter);
+bscan.setRanges(Collections.singleton(new Range()));
+
+for(Entry<Key,Value> entry : bscan) {
+    System.out.println(" " + entry.getKey().getColumnQualifier());
+}
+----
+
+This code effectively has the BatchScanner scan all tablets of a table, 
looking for
+documents that match all the given terms. Because all tablets are being 
scanned for
+every query, each query is more expensive than other Accumulo scans, which
+typically involve a small number of TabletServers. This reduces the number of
+concurrent queries supported and is subject to what is known as the `straggler'
+problem in which every query runs as slow as the slowest server participating.
+
+Of course, fast servers will return their results to the client which can 
display them
+to the user immediately while they wait for the rest of the results to arrive. 
If the
+results are unordered this is quite effective as the first results to arrive 
are as good
+as any others to the user.

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