Hive Camera App For Mac

Hive Camera App
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Table of Contents

Installation and Configuration

You can install a stable release of Hive by downloading a tarball, or you can download the source code and build Hive from that.

Running HiveServer2 and Beeline

Requirements

Java 1.7
Note: Hive versions 1.2 onward require Java 1.7 or newer. Hive versions 0.14 to 1.1 work with Java 1.6 as well. Users are strongly advised to start moving to Java 1.8 (see HIVE-8607).
Hadoop 2.x (preferred), 1.x (not supported by Hive 2.0.0 onward).
Hive versions up to 0.13 also supported Hadoop 0.20.x, 0.23.x.
Hive is commonly used in production Linux and Windows environment. Mac is a commonly used development environment. The instructions in this document are applicable to Linux and Mac. Using it on Windows would require slightly different steps.

Installing Hive from a Stable Release

Start by downloading the most recent stable release of Hive from one of the Apache download mirrors (see Hive Releases).

Next you need to unpack the tarball. This will result in the creation of a subdirectory named hive-x.y.z (where x.y.z is the release number):

Set the environment variable HIVE_HOME to point to the installation directory:

Finally, add $HIVE_HOME/bin to your PATH:

Building Hive from Source

The Hive GIT repository for the most recent Hive code is located here: git clone https://git-wip-us.apache.org/repos/asf/hive.git (the master branch).

All release versions are in branches named 'branch-0.#' or 'branch-1.#' or the upcoming 'branch-2.#', with the exception of release 0.8.1 which is in 'branch-0.8-r2'. Any branches with other names are feature branches for works-in-progress. See Understanding Hive Branches for details.

As of 0.13, Hive is built using Apache Maven.

Compile Hive on master

To build the current Hive code from the master branch:

Here, {version} refers to the current Hive version.

If building Hive source using Maven (mvn), we will refer to the directory '/packaging/target/apache-hive-{version}-SNAPSHOT-bin/apache-hive-{version}-SNAPSHOT-bin' as <install-dir> for the rest of the page.

Compile Hive on branch-1

In branch-1, Hive supports both Hadoop 1.x and 2.x. You will need to specify which version of Hadoop to build against via a Maven profile. To build against Hadoop 1.x use the profile hadoop-1; for Hadoop 2.x use hadoop-2. For example to build against Hadoop 1.x, the above mvn command becomes:

Compile Hive Prior to 0.13 on Hadoop 0.20

Prior to Hive 0.13, Hive was built using Apache Ant. To build an older version of Hive on Hadoop 0.20:

If using Ant, we will refer to the directory 'build/dist' as <install-dir>.

Compile Hive Prior to 0.13 on Hadoop 0.23

To build Hive in Ant against Hadoop 0.23, 2.0.0, or other version, build with the appropriate flag; some examples below:

Running Hive

Hive uses Hadoop, so:

you must have Hadoop in your path OR
export HADOOP_HOME=<hadoop-install-dir>

In addition, you must use below HDFS commands to create /tmp and /user/hive/warehouse (aka hive.metastore.warehouse.dir) and set them chmod g+w before you can create a table in Hive.

You may find it useful, though it's not necessary, to set HIVE_HOME:

Running Hive CLI

To use the Hive command line interface (CLI) from the shell:

Running HiveServer2 and Beeline

Starting from Hive 2.1, we need to run the schematool command below as an initialization step. For example, we can use 'derby' as db type.

HiveServer2 (introduced in Hive 0.11) has its own CLI called Beeline. HiveCLI is now deprecated in favor of Beeline, as it lacks the multi-user, security, and other capabilities of HiveServer2. To run HiveServer2 and Beeline from shell:

Beeline is started with the JDBC URL of the HiveServer2, which depends on the address and port where HiveServer2 was started. By default, it will be (localhost:10000), so the address will look like jdbc:hive2://localhost:10000.

Or to start Beeline and HiveServer2 in the same process for testing purpose, for a similar user experience to HiveCLI:

Running HCatalog

To run the HCatalog server from the shell in Hive release 0.11.0 and later:

To use the HCatalog command line interface (CLI) in Hive release 0.11.0 and later:

For more information, see HCatalog Installation from Tarball and HCatalog CLI in the HCatalog manual.

Running WebHCat (Templeton)

To run the WebHCat server from the shell in Hive release 0.11.0 and later:

For more information, see WebHCat Installation in the WebHCat manual.

Configuration Management Overview

Hive by default gets its configuration from <install-dir>/conf/hive-default.xml
The location of the Hive configuration directory can be changed by setting the HIVE_CONF_DIR environment variable.
Configuration variables can be changed by (re-)defining them in <install-dir>/conf/hive-site.xml
Log4j configuration is stored in <install-dir>/conf/hive-log4j.properties

Hive configuration is an overlay on top of Hadoop – it inherits the Hadoop configuration variables by default.

Hive configuration can be manipulated by:
- Editing hive-site.xml and defining any desired variables (including Hadoop variables) in it
- Using the set command (see next section)
- Invoking Hive (deprecated), Beeline or HiveServer2 using the syntax:
  - $ bin/hive --hiveconf x1=y1 --hiveconf x2=y2 //this sets the variables x1 and x2 to y1 and y2 respectively
  - $ bin/hiveserver2 --hiveconf x1=y1 --hiveconf x2=y2 //this sets server-side variables x1 and x2 to y1 and y2 respectively
  - $ bin/beeline --hiveconf x1=y1 --hiveconf x2=y2 //this sets client-side variables x1 and x2 to y1 and y2 respectively.
- Setting the HIVE_OPTS environment variable to '--hiveconf x1=y1 --hiveconf x2=y2' which does the same as above.

Runtime Configuration

Hive queries are executed using map-reduce queries and, therefore, the behavior of such queries can be controlled by the Hadoop configuration variables.

The HiveCLI (deprecated) and Beeline command 'SET' can be used to set any Hadoop (or Hive) configuration variable. For example:
The latter shows all the current settings. Without the -v option only the variables that differ from the base Hadoop configuration are displayed.

Hive, Map-Reduce and Local-Mode

Hive compiler generates map-reduce jobs for most queries. These jobs are then submitted to the Map-Reduce cluster indicated by the variable:

While this usually points to a map-reduce cluster with multiple nodes, Hadoop also offers a nifty option to run map-reduce jobs locally on the user's workstation. This can be very useful to run queries over small data sets – in such cases local mode execution is usually significantly faster than submitting jobs to a large cluster. Data is accessed transparently from HDFS. Conversely, local mode only runs with one reducer and can be very slow processing larger data sets.

Starting with release 0.7, Hive fully supports local mode execution. To enable this, the user can enable the following option:

In addition, mapred.local.dir should point to a path that's valid on the local machine (for example /tmp/<username>/mapred/local). (Otherwise, the user will get an exception allocating local disk space.)

Starting with release 0.7, Hive also supports a mode to run map-reduce jobs in local-mode automatically. The relevant options are hive.exec.mode.local.auto, hive.exec.mode.local.auto.inputbytes.max, and hive.exec.mode.local.auto.tasks.max:

Note that this feature is disabled by default. If enabled, Hive analyzes the size of each map-reduce job in a query and may run it locally if the following thresholds are satisfied:

The total input size of the job is lower than: hive.exec.mode.local.auto.inputbytes.max (128MB by default)
The total number of map-tasks is less than: hive.exec.mode.local.auto.tasks.max (4 by default)
The total number of reduce tasks required is 1 or 0.

So for queries over small data sets, or for queries with multiple map-reduce jobs where the input to subsequent jobs is substantially smaller (because of reduction/filtering in the prior job), jobs may be run locally.

Note that there may be differences in the runtime environment of Hadoop server nodes and the machine running the Hive client (because of different jvm versions or different software libraries). This can cause unexpected behavior/errors while running in local mode. Also note that local mode execution is done in a separate, child jvm (of the Hive client). If the user so wishes, the maximum amount of memory for this child jvm can be controlled via the option hive.mapred.local.mem. By default, it's set to zero, in which case Hive lets Hadoop determine the default memory limits of the child jvm.

Hive Logging

Hive uses log4j for logging. By default logs are not emitted to the console by the CLI. The default logging level is WARN for Hive releases prior to 0.13.0. Starting with Hive 0.13.0, the default logging level is INFO.

The logs are stored in the directory /tmp/<user.name>:

/tmp/<user.name>/hive.log
Note: In local mode, prior to Hive 0.13.0 the log file name was '.log' instead of 'hive.log'. This bug was fixed in release 0.13.0 (see HIVE-5528 and HIVE-5676).

To configure a different log location, set hive.log.dir in $HIVE_HOME/conf/hive-log4j.properties. Make sure the directory has the sticky bit set (chmod 1777 <dir>).

hive.log.dir=<other_location>

If the user wishes, the logs can be emitted to the console by adding the arguments shown below:

bin/hive --hiveconf hive.root.logger=INFO,console //for HiveCLI (deprecated)
bin/hiveserver2 --hiveconf hive.root.logger=INFO,console

Alternatively, the user can change the logging level only by using:

bin/hive --hiveconf hive.root.logger=INFO,DRFA //for HiveCLI (deprecated)
bin/hiveserver2 --hiveconf hive.root.logger=INFO,DRFA

Another option for logging is TimeBasedRollingPolicy (applicable for Hive 1.1.0 and above, HIVE-9001) by providing DAILY option as shown below:

bin/hive --hiveconf hive.root.logger=INFO,DAILY //for HiveCLI (deprecated)
bin/hiveserver2 --hiveconf hive.root.logger=INFO,DAILY

Note that setting hive.root.logger via the 'set' command does not change logging properties since they are determined at initialization time.

Hive also stores query logs on a per Hive session basis in /tmp/<user.name>/, but can be configured in hive-site.xml with the hive.querylog.location property. Starting with Hive 1.1.0, EXPLAIN EXTENDED output for queries can be logged at the INFO level by setting the hive.log.explain.output property to true.

Logging during Hive execution on a Hadoop cluster is controlled by Hadoop configuration. Usually Hadoop will produce one log file per map and reduce task stored on the cluster machine(s) where the task was executed. The log files can be obtained by clicking through to the Task Details page from the Hadoop JobTracker Web UI.

When using local mode (using mapreduce.framework.name=local), Hadoop/Hive execution logs are produced on the client machine itself. Starting with release 0.6 – Hive uses the hive-exec-log4j.properties (falling back to hive-log4j.properties only if it's missing) to determine where these logs are delivered by default. The default configuration file produces one log file per query executed in local mode and stores it under /tmp/<user.name>. The intent of providing a separate configuration file is to enable administrators to centralize execution log capture if desired (on a NFS file server for example). Execution logs are invaluable for debugging run-time errors.

For information about WebHCat errors and logging, see Error Codes and Responses and Log Files in the WebHCat manual.

Error logs are very useful to debug problems. Please send them with any bugs (of which there are many!) to hive-dev@hadoop.apache.org.

From Hive 2.1.0 onwards (with HIVE-13027), Hive uses Log4j2's asynchronous logger by default. Setting hive.async.log.enabled to false will disable asynchronous logging and fallback to synchronous logging. Asynchronous logging can give significant performance improvement as logging will be handled in a separate thread that uses the LMAX disruptor queue for buffering log messages. Refer to https://logging.apache.org/log4j/2.x/manual/async.html for benefits and drawbacks.

HiveServer2 Logs

HiveServer2 operation logs are available to clients starting in Hive 0.14. See HiveServer2 Logging for configuration.

Audit Logs

Audit logs are logged from the Hive metastore server for every metastore API invocation.

An audit log has the function and some of the relevant function arguments logged in the metastore log file. It is logged at the INFO level of log4j, so you need to make sure that the logging at the INFO level is enabled (see HIVE-3505). The name of the log entry is 'HiveMetaStore.audit'.

Audit logs were added in Hive 0.7 for secure client connections (HIVE-1948) and in Hive 0.10 for non-secure connections (HIVE-3277; also see HIVE-2797).

Perf Logger

In order to obtain the performance metrics via the PerfLogger, you need to set DEBUG level logging for the PerfLogger class (HIVE-12675). This can be achieved by setting the following in the log4j properties file.

log4j.logger.org.apache.hadoop.hive.ql.log.PerfLogger=DEBUG

If the logger level has already been set to DEBUG at root via hive.root.logger, the above setting is not required to see the performance logs.

DDL Operations

The Hive DDL operations are documented in Hive Data Definition Language.

Creating Hive Tables

creates a table called pokes with two columns, the first being an integer and the other a string.

creates a table called invites with two columns and a partition column called ds. The partition column is a virtual column. It is not part of the data itself but is derived from the partition that a particular dataset is loaded into.

By default, tables are assumed to be of text input format and the delimiters are assumed to be ^A(ctrl-a).

Browsing through Tables

lists all the tables.

lists all the table that end with 's'. The pattern matching follows Java regular expressions. Check out this link for documentation http://java.sun.com/javase/6/docs/api/java/util/regex/Pattern.html.

shows the list of columns.

Altering and Dropping Tables

Table names can be changed and columns can be added or replaced:

Note that REPLACE COLUMNS replaces all existing columns and only changes the table's schema, not the data. The table must use a native SerDe. REPLACE COLUMNS can also be used to drop columns from the table's schema:

Dropping tables:

Metadata Store

Metadata is in an embedded Derby database whose disk storage location is determined by the Hive configuration variable named javax.jdo.option.ConnectionURL. By default this location is ./metastore_db (see conf/hive-default.xml).

Right now, in the default configuration, this metadata can only be seen by one user at a time.

Metastore can be stored in any database that is supported by JPOX. The location and the type of the RDBMS can be controlled by the two variables javax.jdo.option.ConnectionURL and javax.jdo.option.ConnectionDriverName. Refer to JDO (or JPOX) documentation for more details on supported databases. The database schema is defined in JDO metadata annotations file package.jdo at src/contrib/hive/metastore/src/model.

In the future, the metastore itself can be a standalone server.

If you want to run the metastore as a network server so it can be accessed from multiple nodes, see Hive Using Derby in Server Mode.

DML Operations

The Hive DML operations are documented in Hive Data Manipulation Language.

Loading data from flat files into Hive:

Loads a file that contains two columns separated by ctrl-a into pokes table. 'LOCAL' signifies that the input file is on the local file system. If 'LOCAL' is omitted then it looks for the file in HDFS.

The keyword 'OVERWRITE' signifies that existing data in the table is deleted. If the 'OVERWRITE' keyword is omitted, data files are appended to existing data sets.

NOTES:

NO verification of data against the schema is performed by the load command.
If the file is in hdfs, it is moved into the Hive-controlled file system namespace.
The root of the Hive directory is specified by the option hive.metastore.warehouse.dir in hive-default.xml. We advise users to create this directory before trying to create tables via Hive.

The two LOAD statements above load data into two different partitions of the table invites. Table invites must be created as partitioned by the key ds for this to succeed.

The above command will load data from an HDFS file/directory to the table.
Note that loading data from HDFS will result in moving the file/directory. As a result, the operation is almost instantaneous.

SQL Operations

The Hive query operations are documented in Select.

Example Queries

Some example queries are shown below. They are available in build/dist/examples/queries.
More are available in the Hive sources at ql/src/test/queries/positive.

SELECTS and FILTERS

selects column 'foo' from all rows of partition ds=2008-08-15 of the invites table. The results are not stored anywhere, but are displayed on the console.

Note that in all the examples that follow, INSERT (into a Hive table, local directory or HDFS directory) is optional.

selects all rows from partition ds=2008-08-15 of the invites table into an HDFS directory. The result data is in files (depending on the number of mappers) in that directory.
NOTE: partition columns if any are selected by the use of *. They can also be specified in the projection clauses.

Partitioned tables must always have a partition selected in the WHERE clause of the statement.

selects all rows from pokes table into a local directory.

selects the sum of a column. The avg, min, or max can also be used. Note that for versions of Hive which don't include HIVE-287, you'll need to use COUNT(1) in place of COUNT(*).

GROUP BY

Note that for versions of Hive which don't include HIVE-287, you'll need to use COUNT(1) in place of COUNT(*).

JOIN

MULTITABLE INSERT

STREAMING

This streams the data in the map phase through the script /bin/cat (like Hadoop streaming).
Similarly – streaming can be used on the reduce side (please see the Hive Tutorial for examples).

Simple Example Use Cases

MovieLens User Ratings

First, create a table with tab-delimited text file format:

Then, download the data files from MovieLens 100k on the GroupLens datasets page (which also has a README.txt file and index of unzipped files):

or:

Note: If the link to GroupLens datasets does not work, please report it on HIVE-5341 or send a message to the user@hive.apache.org mailing list.

Unzip the data files:

And load u.data into the table that was just created:

Count the number of rows in table u_data:

Note that for older versions of Hive which don't include HIVE-287, you'll need to use COUNT(1) in place of COUNT(*).

Now we can do some complex data analysis on the table u_data:

Create weekday_mapper.py:

Use the mapper script:

Note that if you're using Hive 0.5.0 or earlier you will need to use COUNT(1) in place of COUNT(*).

Apache Weblog Data

The format of Apache weblog is customizable, while most webmasters use the default.
For default Apache weblog, we can create a table with the following command.

More about RegexSerDe can be found here in HIVE-662 and HIVE-1719.

What you need

An iPhone, iPad, or iPod Touch with iOS 13.2 or later.
The Home app set up on your iPhone, iPad, or iPod touch with the Apple ID that you use with iCloud.
A home hub set up on an iPad, HomePod, or Apple TV.
One or more HomeKit Secure Video cameras.
A supported iCloud storage plan.

Streaming video and storing video clips don't require an iCloud storage plan. If you want to record your video, you need a 200GB iCloud storage plan to support one camera and a 2TB storage plan to support up to five cameras.

Set up your camera

Check with your camera manufacturer if you need to install their iOS or iPadOS camera app for setup.
Add your camera as an accessory in the Home app. You'll see a notification on your device that says your camera is added. Make sure that you add your camera to the same network that your Home app is on and that the network is stable. If your network disconnects while your camera is recording, your video might not be stored.
Follow the onscreen instructions to choose your streaming and recording settings, user permissions, and more. You can also change these settings later. When you're finished, tap Done.

Use these steps to add all of your HomeKit Secure Video cameras to the Home app. You'll get a notification if you reach the maximum number of recording-enabled cameras supported with your iCloud storage plan.

The Apple ID that you use for the Home app allows up to five total recording-enabled cameras across as many homes as you own. Members of a Family Sharing iCloud storage plan share this limit with everyone else on the storage plan. To record with more than five cameras, you need to use another Apple ID with another iCloud storage plan.

Change your settings

To adjust camera settings, open the Home app on your iPhone, iPad, or iPod touch. Tap the video feed, then tap Settings . On Mac,* Control-click the video feed, then select Settings .

Change the name of your camera and room where it's located. Turn on Include in Favorites to access and control your accessory in the Home tab in the Home app, in Control Center on iOS, iPadOS, or Apple TV, and with some features on your Apple Watch.

Tap Notifications to change how you receive notifications. You can turn notifications on or off, receive them when certain motion is detected, when no one is home, and more. Notification settings might vary by camera manufacturer.

Adjust Recording Options to choose what is recorded and which video clips are stored. Choose to stream and record when household members are at home or away, record with or without sound, or when certain motion is detected. Streaming and recording settings might vary by camera manufacturer.
Turn on Face Recognition to be notified when people you've tagged in the Photos app come to your door.

* You can change recording settings and view video clips and streams in the Home app with macOS 10.15.1 and later.

Review and share your video

View your video streams and recordings in the Home tab of the Home app under Favorite Cameras, or in the room in Home app where you've put the camera. Members of your home can also view video streams in Control Center on Apple TV, in the Home app on Mac, or on Apple Watch. The primary Home app user can restrict which home users can view all video recordings.

To review video clips, tap the video feed in the Home app on your iPhone, iPad, or iPod touch. On Mac,* double-click the video feed in the Home app.

Look above the video to see what time and day you're viewing. Use the calendar above the video to view recordings on a specific day. You can view the last ten days of recorded video clip history that's stored in iCloud.

Use the video timeline at the bottom of the screen to play or pause the recording, or use the timeline to move back or forward in the video. If you see a person, animal, or car icon on the timeline, tap or click the icon to watch a recorded video clip from when your home hub detected motion. On iPhone, iPad, or iPod touch, pinch the timeline with two fingers to zoom in and see your frames in more detail.

To share your video clips with other people, apps, or save them to iCloud or Photos, tap or click .

Tap or click to view other accessories located in the same room as your camera in the Home app.

* You can change recording settings and view video clips and streams in the Home app with macOS 10.15.1 and later.

Do more with the Home app

Hive Camera App

Hive Work App

Set up Face Recognition in the Home app to get notified the moment a visitor arrives at your door.
Invite others to control your home and edit user permissions.
Turn on notifications and check the status of your home.