Tahoe Logging

  1. Overview

  2. Realtime Logging

  3. Incidents

  4. Working with flogfiles

  5. Gatherers

    1. Incident Gatherer

    2. Log Gatherer

  6. Adding log messages

  7. Log Messages During Unit Tests


Tahoe uses the Foolscap logging mechanism (known as the “flog” subsystem) to record information about what is happening inside the Tahoe node. This is primarily for use by programmers and grid operators who want to find out what went wrong.

The Foolscap logging system is documented at https://github.com/warner/foolscap/blob/latest-release/doc/logging.rst.

The Foolscap distribution includes a utility named “flogtool” that is used to get access to many Foolscap logging features. flogtool should get installed into the same virtualenv as the tahoe command.

Realtime Logging

When you are working on Tahoe code, and want to see what the node is doing, the easiest tool to use is “flogtool tail”. This connects to the Tahoe node and subscribes to hear about all log events. These events are then displayed to stdout, and optionally saved to a file.

flogtool tail” connects to the “logport”, for which the FURL is stored in BASEDIR/private/logport.furl . The following command will connect to this port and start emitting log information:

flogtool tail BASEDIR/private/logport.furl

The --save-to FILENAME option will save all received events to a file, where then can be examined later with “flogtool dump” or “flogtool web-viewer”. The --catch-up option will ask the node to dump all stored events before subscribing to new ones (without --catch-up, you will only hear about events that occur after the tool has connected and subscribed).


Foolscap keeps a short list of recent events in memory. When something goes wrong, it writes all the history it has (and everything that gets logged in the next few seconds) into a file called an “incident”. These files go into BASEDIR/logs/incidents/ , in a file named “incident-TIMESTAMP-UNIQUE.flog.bz2”. The default definition of “something goes wrong” is the generation of a log event at the log.WEIRD level or higher, but other criteria could be implemented.

The typical “incident report” we’ve seen in a large Tahoe grid is about 40kB compressed, representing about 1800 recent events.

These “flogfiles” have a similar format to the files saved by “flogtool tail --save-to”. They are simply lists of log events, with a small header to indicate which event triggered the incident.

The “flogtool dump FLOGFILE” command will take one of these .flog.bz2 files and print their contents to stdout, one line per event. The raw event dictionaries can be dumped by using “flogtool dump --verbose FLOGFILE”.

The “flogtool web-viewer” command can be used to examine the flogfile in a web browser. It runs a small HTTP server and emits the URL on stdout. This view provides more structure than the output of “flogtool dump”: the parent/child relationships of log events is displayed in a nested format. “flogtool web-viewer” is still fairly immature.

Working with flogfiles

The “flogtool filter” command can be used to take a large flogfile (perhaps one created by the log-gatherer, see below) and copy a subset of events into a second file. This smaller flogfile may be easier to work with than the original. The arguments to “flogtool filter” specify filtering criteria: a predicate that each event must match to be copied into the target file. --before and --after are used to exclude events outside a given window of time. --above will retain events above a certain severity level. --from retains events send by a specific tubid. --strip-facility removes events that were emitted with a given facility (like foolscap.negotiation or tahoe.upload).


In a deployed Tahoe grid, it is useful to get log information automatically transferred to a central log-gatherer host. This offloads the (admittedly modest) storage requirements to a different host and provides access to logfiles from multiple nodes (web-API, storage, or helper) in a single place.

There are two kinds of gatherers: “log gatherer” and “stats gatherer”. Each produces a FURL which needs to be placed in the NODEDIR/tahoe.cfg file of each node that is to publish to the gatherer, under the keys “log_gatherer.furl” and “stats_gatherer.furl” respectively. When the Tahoe node starts, it will connect to the configured gatherers and offer its logport: the gatherer will then use the logport to subscribe to hear about events.

The gatherer will write to files in its working directory, which can then be examined with tools like “flogtool dump” as described above.

Incident Gatherer

The “incident gatherer” only collects Incidents: records of the log events that occurred just before and slightly after some high-level “trigger event” was recorded. Each incident is classified into a “category”: a short string that summarizes what sort of problem took place. These classification functions are written after examining a new/unknown incident. The idea is to recognize when the same problem is happening multiple times.

A collection of classification functions that are useful for Tahoe nodes are provided in misc/incident-gatherer/support_classifiers.py . There is roughly one category for each log.WEIRD-or-higher level event in the Tahoe source code.

The incident gatherer is created with the “flogtool create-incident-gatherer WORKDIR” command, and started with “tahoe run”. The generated “gatherer.tac” file should be modified to add classifier functions.

The incident gatherer writes incident names (which are simply the relative pathname of the incident-\*.flog.bz2 file) into classified/CATEGORY. For example, the classified/mutable-retrieve-uncoordinated-write-error file contains a list of all incidents which were triggered by an uncoordinated write that was detected during mutable file retrieval (caused when somebody changed the contents of the mutable file in between the node’s mapupdate step and the retrieve step). The classified/unknown file contains a list of all incidents that did not match any of the classification functions.

At startup, the incident gatherer will automatically reclassify any incident report which is not mentioned in any of the classified/\* files. So the usual workflow is to examine the incidents in classified/unknown, add a new classification function, delete classified/unknown, then bound the gatherer with “tahoe restart WORKDIR”. The incidents which can be classified with the new functions will be added to their own classified/FOO lists, and the remaining ones will be put in classified/unknown, where the process can be repeated until all events are classifiable.

The incident gatherer is still fairly immature: future versions will have a web interface and an RSS feed, so operations personnel can track problems in the storage grid.

In our experience, each incident takes about two seconds to transfer from the node that generated it to the gatherer. The gatherer will automatically catch up to any incidents which occurred while it is offline.

Log Gatherer

The “Log Gatherer” subscribes to hear about every single event published by the connected nodes, regardless of severity. This server writes these log events into a large flogfile that is rotated (closed, compressed, and replaced with a new one) on a periodic basis. Each flogfile is named according to the range of time it represents, with names like “from-2008-08-26-132256--to-2008-08-26-162256.flog.bz2”. The flogfiles contain events from many different sources, making it easier to correlate things that happened on multiple machines (such as comparing a client node making a request with the storage servers that respond to that request).

Create the Log Gatherer with the “flogtool create-gatherer WORKDIR” command, and start it with “twistd -ny gatherer.tac”. Then copy the contents of the log_gatherer.furl file it creates into the BASEDIR/tahoe.cfg file (under the key log_gatherer.furl of the section [node]) of all nodes that should be sending it log events. (See Configuring a Tahoe-LAFS node)

The “flogtool filter” command, described above, is useful to cut down the potentially large flogfiles into a more focussed form.

Busy nodes, particularly web-API nodes which are performing recursive deep-size/deep-stats/deep-check operations, can produce a lot of log events. To avoid overwhelming the node (and using an unbounded amount of memory for the outbound TCP queue), publishing nodes will start dropping log events when the outbound queue grows too large. When this occurs, there will be gaps (non-sequential event numbers) in the log-gatherer’s flogfiles.

Adding log messages

When adding new code, the Tahoe developer should add a reasonable number of new log events. For details, please see the Foolscap logging documentation, but a few notes are worth stating here:

  • use a facility prefix of “tahoe.”, like “tahoe.mutable.publish

  • assign each severe (log.WEIRD or higher) event a unique message identifier, as the umid= argument to the log.msg() call. The misc/coding_tools/make_umid script may be useful for this purpose. This will make it easier to write a classification function for these messages.

  • use the parent= argument whenever the event is causally/temporally clustered with its parent. For example, a download process that involves three sequential hash fetches could announce the send and receipt of those hash-fetch messages with a parent= argument that ties them to the overall download process. However, each new web-API download request should be unparented.

  • use the format= argument in preference to the message= argument. E.g. use log.msg(format="got %(n)d shares, need %(k)d", n=n, k=k) instead of log.msg("got %d shares, need %d" % (n,k)). This will allow later tools to analyze the event without needing to scrape/reconstruct the structured data out of the formatted string.

  • Pass extra information as extra keyword arguments, even if they aren’t included in the format= string. This information will be displayed in the “flogtool dump --verbose” output, as well as being available to other tools. The umid= argument should be passed this way.

  • use log.err for the catch-all addErrback that gets attached to the end of any given Deferred chain. When used in conjunction with LOGTOTWISTED=1, log.err() will tell Twisted about the error-nature of the log message, causing Trial to flunk the test (with an “ERROR” indication that prints a copy of the Failure, including a traceback). Don’t use log.err for events that are BAD but handled (like hash failures: since these are often deliberately provoked by test code, they should not cause test failures): use log.msg(level=BAD) for those instead.

Log Messages During Unit Tests

If a test is failing and you aren’t sure why, start by enabling FLOGTOTWISTED=1 like this:


With FLOGTOTWISTED=1, sufficiently-important log events will be written into _trial_temp/test.log, which may give you more ideas about why the test is failing.

By default, _trial_temp/test.log will not receive messages below the level=OPERATIONAL threshold. You can change the threshold via the FLOGLEVEL variable, e.g.:


(The level numbers are listed in src/allmydata/util/log.py.)

To look at the detailed foolscap logging messages, run the tests like this:


The first environment variable will cause foolscap log events to be written to ./flog.out.bz2 (instead of merely being recorded in the circular buffers for the use of remote subscribers or incident reports). The second will cause all log events to be written out, not just the higher-severity ones. The third will cause twisted log events (like the markers that indicate when each unit test is starting and stopping) to be copied into the flogfile, making it easier to correlate log events with unit tests.

Enabling this form of logging appears to roughly double the runtime of the unit tests. The flog.out.bz2 file is approximately 2MB.

You can then use “flogtool dump” or “flogtool web-viewer” on the resulting flog.out file.

(”flogtool tail” and the log-gatherer are not useful during unit tests, since there is no single Tub to which all the log messages are published).

It is possible for setting these environment variables to cause spurious test failures in tests with race condition bugs. All known instances of this have been fixed as of Tahoe-LAFS v1.7.1.