The log analyzer is a simple but powerful Python 3 script for analyzing a set of Apama correlator log files and extracting useful diagnostic information.
Features:
The starting point after analyzing a log is to look at
overview.html, an HTML overview containing interactive charts for the main statistics such as event rate and memory usage, as well as a textual summary (also inoverview.txt) of each log file and details such as host:port, timezone, etc from the correlator's startup lines. This page also has links to files such aslogged_errors/warnings.txtthat you'll also want to look at. The analyzer calculates and plots some statistics derived from the values in theCorrelator Status:lines such as:- rx/tx rate /sec, which are useful for determining typical receive/send rates and any anomalous periods of high/low/zero rates
- log lines /sec, which is useful for detecting excessive logging
- warn and error lines /sec, which is useful for identifying periods where bad things happened (error includes both ERROR and FATAL levels)
- is swapping, which is 1 if any swapping in or out is occurring or 0 if not; swapping is the most common cause of performance problems and disconnections
If you have several log files, pass them all to the log analyzer at the same time, as that makes it easier to notice important differences between the logs (e.g. different timezones or available memory/CPUs). It's also a good way of confirming which log files actually contain the time period where the problem occurred!
logged_errors.txt/logged_warnings.txt: Summarizes WARN and ERROR/FATAL messages across multiple log files, de-duplicating (by removing numeric bits from the message) and displaying the time range where each error/warning occurred in each log file.This makes it easy to skim past unimportant errors/warnings and spot the ones that really matter, and to correlate them with the times during which the problem occurred.
receiver_connections.XXX.csv: Extract log messages about connections, disconnections and slowness in receivers.This can be very useful for debugging slow receiver disconnections.
Supported Apama releases: Apama 4.3 through to latest (10.11+). Also works with correlator logging from Apama-ctrl, downloaded from Cumulocity.
Licensed under the Apache License 2.0.
There are also some additional files which may be useful for more advanced cases:
status.XXX.csv: Extracts all periodic statistics from "Correlator Status:" lines, exporting them to an Excel-friendly CSV file. Columns are named in a user-friendly way, and some derived stats such as event rate are calculated. The header line contains additional metadata such as machine info, host:port and timezone.summary_status.XXX.csv: Generates a small summary CSV file containing a snapshot of values from the start/middle/end of each log, min/mean/max aggregate values, and deltas between them. This is a good first port of call, to check which columns might be worth graphing from the main status CSV to chase down a memory leak or unresponsive application.startup_stanza.XXX.log: A copy of the first few lines of the log file that contain critical startup information such as host/port/name/configuration. If the log file does not contain startup information (perhaps it only contains recent log messages) this file will be missing. As a missing startup stanza impairs some functionality of this tool, try to obtain the missing startup information if at all possible.- JSON output mode. Most of the above can also be written in JSON format if desired, for post-processing by other scripts. Alternatively, the
apamax.log_analyzer.LogAnalyzer(orLogAnalyzerTool) Python class can be imported and subclassed or used from your own Python scripts.
Download the latest stable version of the script from https://github.com/ApamaCommunity/apama-log-analyzer/releases
To run the script, simply execute the script with Python 3, specifying all log file(s) and/or archived log files and/or directories to be analyzed:
> log_analyzer.py mycorrelator1.log mycorrelator2.log my_zipped_logs.zip mylogdirectory/*.log
On Linux, make sure python3 is on PATH. On Windows, ensure you have a .py file association for (or explicitly run it with) py.exe or python.exe from a Python 3 installation. Apama releases from 10.3.0 onwards contain Python 3, so an Apama command prompt/apama_env shell will have the correct python.exe/python3 on PATH. If you don't have Apama 10.3.0 available, you can download Python 3.6+ yourself. No other Python packages are required. If you get a SyntaxError or similar, you might be running it with Python 2 by mistake.
Start by reviewing the overview.txt (which is also displayed on stdout when you've run the tool), then identify which logs and columns you'd like to graph (status_summary.XXX.csv may help with this), and then open the relevant status.XXX.csv file in a spreadsheet such as Excel. The logged_errors.txt and logged_warnings.txt files are also worth reviewing carefully.
For information about the meaning of the status lines which may be helpful when analyzing the csv files, see the Resources section below.
Note that the overview.html page uses the http://dygraphs.com JavaScript library to display the charts, and these are downloaded from the internet when the page is opened, so you will need an internet connection to open the overview.html page correctly (though you don't need one to run the analyzer).
In addition to the standard "Correlator status:" lines, the tool can perform similar extraction for any user-defined status lines. For example this could be used for the correlator's persistence or JMS status lines, or for your own lines logged regularly at INFO level by your own EPL to show some application KPIs.
To do this create a .json configuration file containing a "userStatusLines" dicionary and pass it to the tool using
--config. For example:
{
"userStatusLines":{
// This is for a typical application-defined status line. Note that any number (or text) inside [...] brackets (
// typically the monitor instance id) is ignored. This is matched against the "message" part of the log line,
// which follows the first " - " in the line (except for [apama-ctrl] messages which have an extra <apama-ctrl> prefix)
"com.mycompany.MyMonitor [1] MyApplication Status:": {
// This prefix is added to the start of each alias to avoid clashes with other status KPIs
"fieldPrefix":"myApp.",
// Specifying user-friendly aliases for each is optional. Always include any units (e.g. MB) in the field name or alias
"field:alias":{
"kpi1":"",
"kpi2":"kpi2AliasWithUnits",
"kpi3":""
}},
// This detects INFO level lines beginning with "JMS Status:"
"JMS Status:": {
"fieldPrefix":"jms.",
"field:alias":{
"s":"s=senders",
"r":"r=receivers",
"rRate":"rx /sec",
"sRate":"tx /sec",
"rWindow":"receive window",
"rRedel":"redelivered",
"rMaxDeliverySecs":"",
"rDupsDet":"",
"rDupIds":"",
"connErr":"",
"jvmMB":""
}},
// Similarly for persistence
"Persistence Status:": {
"fieldPrefix":"p.",
"field:alias":{
"numSnapshots":"",
"lastSnapshotTime":"",
"snapshotWaitTimeEwmaMillis":"",
"commitTimeEwmaMillis":"",
"lastSnapshotRowsChangedEwma":""
}}
}
// JMS per-receiver detailed status lines - also demonstrates creating numbered columns for
// a dynamic set of status lines each identified by a unique key
" JMSReceiver ":
{
// The ?P<key> named group in this regular expression identifies the key for which a uniquely numbered set of columns will be created
"keyRegex": " *(?P<key>[^ :]+): rx=",
// Estimates the number of keys to allocate columns for; if more keys are required, the file will be reparsed with double the number
"maxKeysToAllocateColumnsFor": 2,
"fieldPrefix":"jmsReceiver.",
"key:alias":{
"rRate":"rx /sec",
"rWindow":"receive window",
"rRedel":"redelivered",
"rMaxDeliverySecs":"",
"rDupsDet":"",
"rDupIds":"",
"msgErrors":"",
"jvmMB":"",
// special values that can be added if desired, or for debugging
"line num":"",
// Computed values begin with "=". Currently the only supported type is "FIELDNAME /sec" for calculating rates
"=msgErrors /sec": ""
}},
}
Any user-defined status lines should be of the same form as the Correlator status lines, logged at INFO level, for example:
on all wait(5.0) {
log "MyApplication Status:"
+" kpi1="+kpi1.toString()
+" kpi2="+kpi2.toString()
+" kpi3=\""+kpi3+"\"" at INFO;
}
Technical detail: the frequency and timing of other status lines may not match when the main "Correlator status:" lines are logged. The analyzer just uses the main status lines for the timing, adding the most recently seen user status values and recording them in a single row with timing and line information from the main status lines.
In addition to the standard charts, you can add charts with an mix of user-defined and standard status values. This is achieved using the JSON configuration file described above with a "userCharts" entry. For example:
{
"userStatusLines":{
// ...
},
"userCharts": {
// Each chart is described by "uniqueid": { "heading": "title", "labels": [keys], other options... }
"jms_rates":{
"heading":"JMS rates",
"labels":["jms.rx /sec", "jms.tx /sec"],
"colors":["red", "pink", "orange"],
"ylabel":"Events/sec",
// For big numbers this often looks better than exponential notation
"labelsKMB":true
},
// Colors are decided automatically by default, but can be overridden
// This example shows how to put some series onto a y axis
"persistence":{
"heading":"Correlator persistence",
"labels":["p.numSnapshots", "p.snapshotWaitTimeEwmaMillis", "p.commitTimeEwmaMillis"],
"colors":["red", "green", "blue"],
"ylabel":"Time (ms)",
"y2label":"Number of snapshots",
"series": {"p.numSnapshots":{"axis":"y2"}}
}
}
}
If you're using Apama inside Cumulocity, to download the log use the App Switcher icon to go to Administration, then Applications > Subscribed applications > Apama-ctrl-XXX. Assuming Apama-ctrl is running, you'll see a Logs tab. You should try to get the full log - to do that click the |<< button to find out the date of the first entry then click Download, and select the time range from the start date to the day after today.
When you open a CSV file in Excel, to automatically resize all columns so that their contents can be viewed just select all (Ctrl+A), then double-click the separator between any two of the column headings.
In recent versions of Excel, selecting cell B2 and then View > Freeze Panes > Freeze Panes is useful for ensuring the datetime column and header row are always visible as you scroll.
It may be worth adding a trendline to your Excel charts to smooth out any short-term artifacts. For example, given that status lines are logged every 5 seconds, a moving average trendline with a period of 6 samples (=30s), 12 samples (=60s) or 24 samples (=2m) can be useful when graphing the send (tx) rate in cases where the rate appears to be modal over two or three values (as a result of the interaction between the 5 second log sample period and the batching of message sending within the correlator).
Unfortunately the CSV file format (and Excel in particular) has fairly poor support for use in locales such as German that have different decimal, thousand and date formats to the US/UK format generated by this tool. It is therefore necessary to explicitly tell Excel how to interpret the numeric CSV columns. In Excel 365, the steps are:
- Open Excel (it should be displaying an empty spreadsheet; don't open the CSV file yet).
- On the Data tab click From Text/CSV and select the CSV file to be imported.
- Ensure the Delimiter is set to Comma, then click Edit.
- On the Home tab of the Power Query Editor dialog, click Use First Row as Headers.
- Select all columns that contain numbers. To do this click the heading for
epoch secs, scroll right until you see# metadata:then hold down SHIFT and click the column before# metadata:. - (Optional: if you plan to use any values containing non-numeric data (e.g. slowest consumer or context name) then deselect those columns by holding down CTRL while clicking them; otherwise non-numeric values will show up as _Error_ or blank).
- Right-click the selected column headings, and choose Change Type > Using Locale....
- Set the Data Type to Decimal Number and Locale to English (Australia) (or United States; any English locale should be fine), then click OK.
- On the Home tab click Close & Load.
From the Apama documentation:
- List of Correlator Status Statistics - for understanding the meaning of the statistics available
- Inspecting correlator state - for using the engine_inspect tool to get detailed information on the number of monitor instances, listeners, etc, which can help to identify application memory leaks
- Shutting down and managing components and its child topics - contain information on using dorequest to get detailed memory/CPU profiles, a string representation of the correlator queues, and various enhanced logging options
Please feel free to add suggestions as GitHub tickets, or to contribute a fix or feature yourself (just send a pull request).
If you want to submit a pull request, be sure to run the existing tests, create new tests (and check the coverage is good), and do a before-and-after run of the performance tests to avoid unwittingly making it slower.
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