Challenges
There were a series of challenges we faced in implementing request traceability:
- The Implications of Using Logging with a Multi-threaded Application: Since the LOS catered to multiple-user requests running concurrently, it will usually have more than one thread running on any given tier. But all the threads log into the same log file, which means the log statements will obviously get interleaved. So how do you distinguish between log statements belonging to one request from another?
Logging thread IDs with each log statement could have been an easy solution, but the application server's Thread Pool usually reuses the same thread instance for different client requests. Hence, the thread ID is not a good choice to precisely identify sets of logs statements for any given client request.
- Correlating Log Statements from Distributed Log Files: In an SOA, a typical single user request needs to access the services of different service components potentially deployed on different machines. When a request spans across different servers, how do you correlate the log statements from multiple log files belonging to separate service components, yet pertaining to a single client request?
LRID Generation and Propagation
There was one solution that overcame both problems: generating a unique ID for every client request that enters the system and then propagating this ID across remote calls/Web services serviced by different service components. Henceforth, this unique ID will be referred as LRID (Log Reference ID).
- What constitutes an LRID?
We used the GUID generation algorithm to generate a unique number for every client request. This unique number is prefixed with the login ID with which the thread is associated. The presence of the login ID in the LRID makes it easy to map the LRID to a user.
For example:
"joe--4028dd0:d767dc:109f8bbb3c9:-8000"
identifies Joe as the logged in user.
- When do you generate LRID?
Generating the LRID at the entry point of the client request using a servlet filter's doFilter() method is a good method. You then store this value in the Log4j MDC (mapped diagnostic contexts, which use thread local variables internally). Log4j makes the job of outputting this value with every log statement very easy because of its conversion pattern:
[PRIORITY]: %p%n[DATE]: %d{dd-MMM-yy HH:mm:ss}%n[MESSAGE]:
%m%n[Thread]: %t%n[LRID]:%X{LRID}%n%n.
"LRID" is the key name that is used to store the LRID value in MDC.
Here's an example of a log statement retrieved from the Loan Manager log file:
[PRIORITY]: INFO
[DATE]: 14-Mar-06 18:00:09
[MESSAGE]: Entering in List tavant.app.asap.loanmanager.LoanMangerBean.
retrieveLiabilitiesForLoan(String)
[Thread]: ExecuteThread: '13' for queue: 'weblogic.kernel.Default'
[LRID]: joe--4028dd0:d767dc:109f8bbb3c9:-8000
As a related note, you need to clean the LRID from the MDC at the finish of the doFilter() method.
- How do you propagate LRID across the service components?
This was by far the most interesting and tricky of all the problems we encountered while creating the logging framework because there are no simple solutions to propagating custom data from the servlet layer to the business tier. JSR 149 attempts to solve this problem by standardizing a way in which application-specific properties can be propagated across remote calls, but not much is happening on this JSR. Using Portable Interceptors is a possibility, but that seemed hopelessly involved and difficult. In the end, we came up with two different solutions, each having their own limitations.
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