This page is for administrators and application designers who need data on how the system or their application is running.

Overview

The System Configuration option Log Statistics generates processing statistics into a database table.  We recommend this option is always ticked because log statistics are very useful:

• when troubleshooting problems
• when tuning applications or the PhixFlow system to improve performance.

Statistics Table Structure

Processing statistics are generated in a single table, stats.

The table structure for stats is as follows:

Statistics Dimensions

The performance statistics are classified by 4 major dimensions:

• Initiator: the high-level object initiating the system activity e.g. Task Plan
• Context: the low-level object with which the system activity is associated e.g. Pipe
• Stats Type: the aspect of system behaviour being measured e.g. database read times
• Sample DateTime: Aggregate values apply to a period between a start and end time. Spot values apply to the end time.

Initiator

The initiator is the object representing the user activity that caused the system activity.

Initiators include:

• running
  • an action
  • a task plan
  • a

• • table
• viewing data for a

• view
• the system- for internal activities and things that can't be allocated to any specific cause e.g. memory and CPU usage.

Where one initiator could include another, the activity is recorded against the initiator closest to the user. For example:

• the user runs an action which runs a task plan - the initiator is the action.
• the user runs the same task plan directly - the initiator is the task plan

With the exception of system, the initiator is identified by name, type and id.

Context

The context is the most detailed object to which the activity can be attributed.

Contexts include:

• table-action
• task plan

• table
• pipe
• collectors: file, database or HTTP
• exporters: file, database or HTTP
• system

With the exception of system, the context is identified by name, full name, type and id.

The full name is a dot-separated list of objects from the highest level to the lowest, ending in the context object.

Stats Type / Data Type

Stats Type is the specific aspect of system behaviour being measured.

Data Type

Data type contains the units of the measurement.

Data types that represent a snapshot value are shown as the plural unit e.g. the Data Type for the amount of Java memory used is 'bytes'.

Data types that represent a rate, or amount of throughput are shown as the average rate per second e.g. the data type for the number of items generated in a

table is 'items/s' i.e. items per second. The value is calculated by dividing the total number or amount recorded in the sampling period by the duration of the sampling period in seconds. The rate per second is shown rather than the absolute number so that if the sampling period is changed, the numbers (the rate per second) stay in the same range.

Data types include:

Stats Type

Each Stats Types record a single type of data. The data type is stored in the data as an indicator of the units of the data value for each stats type.

Activity

Activity stats record information about the high-level activities that the system was performing at any time. In general these are user-level actions e.g. running a Task Plan.

Database

Database stats record aggregate values for low-level database operations.

Data Generation

Data Generation stats record details of

table data generation.

Data Output

These stats record details of the output phase of Data Generation, in which items are written to an output queue so that they can be written out by one or more asynchronous writer processes.

Pipes (Pull)

These stats types record various aspects of the behaviour of pull pipes

Pipes (Lookups)

These stats types record various aspects of the behaviour of lookup pipes.

System Performance

System stats record aspects of system behaviour.

Work Queues

PhixFlow maintains a number of queues for asynchronous task work / task processing.

These statistics record the occupancy levels of these queues, comprising for each queue the number of tasks queued (waiting) + the number running.

Examples

These are examples of extracting specific sub-sets of the stats data.

These examples show the use of direct sql statements to extract useful sub-sets of the stats data, but you could also import all data into a

table and analyse it there.

Monitoring Memory Usage

For example, to extract Java memory total and free:

select *
from stats
where stats_type in ('java.memory.free', 'java.memory.total')
order by to_dtm;

Monitoring Pipe Cache Sizes

For example, to extract all pipe caches over a certain size:

select *
from stats
where stats_type = 'lookup.size.items' and data_value>1000000
order by full_context, to_dtm;