Search results for zenoss

ZenPacks.zenoss.PostgreSQL-5.0.5_1.0.9dev-1.lbn19.noarch

Once the PostgreSQL ZenPack is installed you will have the following new zProperties which should be set either for device classes or individual devices. * zPostgreSQLPort - Port where PostgreSQL is listening. Default: 5432 * zPostgreSQLUseSSL - Whether to use SSL or not. Default: False * zPostgreSQLUsername - Must be a superuser. Default: postgres * zPostgreSQLPassword - Password for user. No default. In addition to setting these properties you must add the zenoss.PostgreSQL modeler plugin to a device class or individual device. This modeler plugin will discover all databases and tables using the connectivity information provided through the above settings. Each database and table will automatically be monitored. The following elements are discovered: * Databases * Tables The following performance metrics are collected. * Server * Summaries of all databases and tables. * Databases * Latencies * Connection * SELECT 1 * Connections * Total * Active * Idle * Durations * Active Transactions (min/avg/max) * Idle Transactions (min/avg/max) * Queries (min/avg/max) * Size * Backends * Efficiencies * Transaction Rollback Percentage * Tuple Fetch Percentage * Transaction Rates * Commits/sec * Rollbacks/sec * Tuple Rates * Returned/sec * Fetched/sec * Inserted/sec * Updated/sec * Deleted/sec * Locks * Total * Granted * Waiting * Exclusive * AccessExclusive * Summaries of all tables. * Tables * Scan Rates * Sequential/sec * Indexed/sec * Tuple Rates * Sequentially Read/sec * Index Fetched/sec * Inserted/sec * Updated/sec * Hot Updated/sec * Deleted/sec * Tuples * Live * Dead

ZenPacks.zenoss.PostgreSQL-4.2.5_1.0.8dev-1.lbn19.noarch

Once the PostgreSQL ZenPack is installed you will have the following new zProperties which should be set either for device classes or individual devices. * zPostgreSQLPort - Port where PostgreSQL is listening. Default: 5432 * zPostgreSQLUseSSL - Whether to use SSL or not. Default: False * zPostgreSQLUsername - Must be a superuser. Default: postgres * zPostgreSQLPassword - Password for user. No default. In addition to setting these properties you must add the zenoss.PostgreSQL modeler plugin to a device class or individual device. This modeler plugin will discover all databases and tables using the connectivity information provided through the above settings. Each database and table will automatically be monitored. The following elements are discovered: * Databases * Tables The following performance metrics are collected. * Server * Summaries of all databases and tables. * Databases * Latencies * Connection * SELECT 1 * Connections * Total * Active * Idle * Durations * Active Transactions (min/avg/max) * Idle Transactions (min/avg/max) * Queries (min/avg/max) * Size * Backends * Efficiencies * Transaction Rollback Percentage * Tuple Fetch Percentage * Transaction Rates * Commits/sec * Rollbacks/sec * Tuple Rates * Returned/sec * Fetched/sec * Inserted/sec * Updated/sec * Deleted/sec * Locks * Total * Granted * Waiting * Exclusive * AccessExclusive * Summaries of all tables. * Tables * Scan Rates * Sequential/sec * Indexed/sec * Tuple Rates * Sequentially Read/sec * Index Fetched/sec * Inserted/sec * Updated/sec * Hot Updated/sec * Deleted/sec * Tuples * Live * Dead

ZenPacks.zenoss.PostgreSQL-4.2.5_1.0.8dev-1.lbn19.noarch

Once the PostgreSQL ZenPack is installed you will have the following new zProperties which should be set either for device classes or individual devices. * zPostgreSQLPort - Port where PostgreSQL is listening. Default: 5432 * zPostgreSQLUseSSL - Whether to use SSL or not. Default: False * zPostgreSQLUsername - Must be a superuser. Default: postgres * zPostgreSQLPassword - Password for user. No default. In addition to setting these properties you must add the zenoss.PostgreSQL modeler plugin to a device class or individual device. This modeler plugin will discover all databases and tables using the connectivity information provided through the above settings. Each database and table will automatically be monitored. The following elements are discovered: * Databases * Tables The following performance metrics are collected. * Server * Summaries of all databases and tables. * Databases * Latencies * Connection * SELECT 1 * Connections * Total * Active * Idle * Durations * Active Transactions (min/avg/max) * Idle Transactions (min/avg/max) * Queries (min/avg/max) * Size * Backends * Efficiencies * Transaction Rollback Percentage * Tuple Fetch Percentage * Transaction Rates * Commits/sec * Rollbacks/sec * Tuple Rates * Returned/sec * Fetched/sec * Inserted/sec * Updated/sec * Deleted/sec * Locks * Total * Granted * Waiting * Exclusive * AccessExclusive * Summaries of all tables. * Tables * Scan Rates * Sequential/sec * Indexed/sec * Tuple Rates * Sequentially Read/sec * Index Fetched/sec * Inserted/sec * Updated/sec * Hot Updated/sec * Deleted/sec * Tuples * Live * Dead

ZenPacks.zenoss.PySamba-4.2.4_1.0.2-6.lbn13.x86_64

This package provides the python interface to the Samba C library and executable targets used by other ZenPacks for Windows device monitoring. This package does not itself provide any additional functionality for Zenoss.

ZenPacks.zenoss.PythonCollector-4.2.5_1.6.0-4.lbn13.noarch

This ZenPack provides a new Python data source type. It also provides a new zenpython collector daemon that is responsible for collecting these data sources. The goal of the Python data source type is to replicate some of the standard COMMAND data source type's functionality without requiring a new shell and shell subprocess to be spawned each time the data source is collected. The COMMAND data source type is infinitely flexible, but because of the shell and subprocess spawning, it's performance and ability to pass data into the collection script are limited. The Python data source type circumvents the need to spawn subprocesses by forcing the collection code to be asynchronous using the Twisted library. It circumvents the problem with passing data into the collection logic by being able to pass any basic Python data type without the need to worry about shell escaping issues. The Python data source type is intended to be used in one of two ways. The first way is directly through the creation of Python data sources through the web interface or in a ZenPack. When used in this way, it is the responsibility of the data source creator to implement the required Python class specified in the data source's Python Class Name property field. The second way the Python data source can be used is as a base class for another data source type. Used in this way, the ZenPack author will create a subclass of PythonDataSource to provide a higher-level functionality to the user. The user is then not responsible for writing a Python class to collect and process data.

ZenPacks.zenoss.PythonCollector-5.0.5_1.8.0dev-4.lbn19.noarch

This ZenPack provides a new Python data source type. It also provides a new zenpython collector daemon that is responsible for collecting these data sources. The goal of the Python data source type is to replicate some of the standard COMMAND data source type's functionality without requiring a new shell and shell subprocess to be spawned each time the data source is collected. The COMMAND data source type is infinitely flexible, but because of the shell and subprocess spawning, it's performance and ability to pass data into the collection script are limited. The Python data source type circumvents the need to spawn subprocesses by forcing the collection code to be asynchronous using the Twisted library. It circumvents the problem with passing data into the collection logic by being able to pass any basic Python data type without the need to worry about shell escaping issues. The Python data source type is intended to be used in one of two ways. The first way is directly through the creation of Python data sources through the web interface or in a ZenPack. When used in this way, it is the responsibility of the data source creator to implement the required Python class specified in the data source's Python Class Name property field. The second way the Python data source can be used is as a base class for another data source type. Used in this way, the ZenPack author will create a subclass of PythonDataSource to provide a higher-level functionality to the user. The user is then not responsible for writing a Python class to collect and process data.

ZenPacks.zenoss.PythonCollector-4.2.5_1.6.0-4.lbn19.noarch

This ZenPack provides a new Python data source type. It also provides a new zenpython collector daemon that is responsible for collecting these data sources. The goal of the Python data source type is to replicate some of the standard COMMAND data source type's functionality without requiring a new shell and shell subprocess to be spawned each time the data source is collected. The COMMAND data source type is infinitely flexible, but because of the shell and subprocess spawning, it's performance and ability to pass data into the collection script are limited. The Python data source type circumvents the need to spawn subprocesses by forcing the collection code to be asynchronous using the Twisted library. It circumvents the problem with passing data into the collection logic by being able to pass any basic Python data type without the need to worry about shell escaping issues. The Python data source type is intended to be used in one of two ways. The first way is directly through the creation of Python data sources through the web interface or in a ZenPack. When used in this way, it is the responsibility of the data source creator to implement the required Python class specified in the data source's Python Class Name property field. The second way the Python data source can be used is as a base class for another data source type. Used in this way, the ZenPack author will create a subclass of PythonDataSource to provide a higher-level functionality to the user. The user is then not responsible for writing a Python class to collect and process data.

ZenPacks.zenoss.PythonCollector-4.2.5_1.6.0-4.lbn19.noarch

This ZenPack provides a new Python data source type. It also provides a new zenpython collector daemon that is responsible for collecting these data sources. The goal of the Python data source type is to replicate some of the standard COMMAND data source type's functionality without requiring a new shell and shell subprocess to be spawned each time the data source is collected. The COMMAND data source type is infinitely flexible, but because of the shell and subprocess spawning, it's performance and ability to pass data into the collection script are limited. The Python data source type circumvents the need to spawn subprocesses by forcing the collection code to be asynchronous using the Twisted library. It circumvents the problem with passing data into the collection logic by being able to pass any basic Python data type without the need to worry about shell escaping issues. The Python data source type is intended to be used in one of two ways. The first way is directly through the creation of Python data sources through the web interface or in a ZenPack. When used in this way, it is the responsibility of the data source creator to implement the required Python class specified in the data source's Python Class Name property field. The second way the Python data source can be used is as a base class for another data source type. Used in this way, the ZenPack author will create a subclass of PythonDataSource to provide a higher-level functionality to the user. The user is then not responsible for writing a Python class to collect and process data.

ZenPacks.zenoss.RabbitMQ-4.2.5_1.1.0dev-1.lbn13.noarch

To start monitoring your RabbitMQ server you will need to setup SSH access so that your Zenoss collector server will be able to SSH into your RabbitMQ server(s) as a user who has permission to run the rabbitmqctl command. This almost always means the root user. See the Using a Non-Root User section below for instructions on allowing non-root users to run rabbitmqctl. To do this you need to set the following zProperties for the RabbitMQ devices or their device class in Zenoss. * zCommandUsername * zCommandPassword * zKeyPath The zCommandUsername property must be set. To use public key authentication you must verify that the public portion of the key referenced in zKeyPath is installed in the ~/.ssh/authorized_keys file for the appropriate user on the RabbitMQ server. If this key has a passphrase you should set it in the zCommandPassword property. If you'd rather use password authentication than setup keys, simply put the user's password in the zCommandPassword property. You should then add the zenoss.ssh.RabbitMQ modeler plugin to the device, or device class containing your RabbitMQ servers and remodel the device(s). This will automatically find the node, vhosts, exchanges and queues and begin monitoring them immediately for the following metrics. * Node Values o Status - Running or not? Generates event on failure. o Open Connections & Channels o Sent & Received Bytes Rate o Sent & Received Messages Rate o Depth of Send Queue o Consumers o Unacknowledged & Uncommitted Messages * Queue Values o Ready, Unacknowledged & Total Messages o Memory Usage o Consumers There is a default threshold of 1,000,000 messages per queue. This is almost certainly an absurdly high threshold that shouldn't trip in normal systems. However, by clicking into the details of any individual queue you can set the per-queue threshold to a more reasonable value that makes sense for a given queue.

ZenPacks.zenoss.RabbitMQ-5.0.5_1.1.0dev-3.lbn19.noarch

To start monitoring your RabbitMQ server you will need to setup SSH access so that your Zenoss collector server will be able to SSH into your RabbitMQ server(s) as a user who has permission to run the rabbitmqctl command. This almost always means the root user. See the Using a Non-Root User section below for instructions on allowing non-root users to run rabbitmqctl. To do this you need to set the following zProperties for the RabbitMQ devices or their device class in Zenoss. * zCommandUsername * zCommandPassword * zKeyPath The zCommandUsername property must be set. To use public key authentication you must verify that the public portion of the key referenced in zKeyPath is installed in the ~/.ssh/authorized_keys file for the appropriate user on the RabbitMQ server. If this key has a passphrase you should set it in the zCommandPassword property. If you'd rather use password authentication than setup keys, simply put the user's password in the zCommandPassword property. You should then add the zenoss.ssh.RabbitMQ modeler plugin to the device, or device class containing your RabbitMQ servers and remodel the device(s). This will automatically find the node, vhosts, exchanges and queues and begin monitoring them immediately for the following metrics. * Node Values o Status - Running or not? Generates event on failure. o Open Connections & Channels o Sent & Received Bytes Rate o Sent & Received Messages Rate o Depth of Send Queue o Consumers o Unacknowledged & Uncommitted Messages * Queue Values o Ready, Unacknowledged & Total Messages o Memory Usage o Consumers There is a default threshold of 1,000,000 messages per queue. This is almost certainly an absurdly high threshold that shouldn't trip in normal systems. However, by clicking into the details of any individual queue you can set the per-queue threshold to a more reasonable value that makes sense for a given queue.