As described in the issue ThreadScope#29, the ThreadScope is really slow when loading a relatively huge eventlog file, and consumed an unacceptable volume of memory. High memory usage caused by using incorrect data structure is fairly common in many Haskell applications, thus at first glance, I focused on the internal data structure that ThreadScope uses when browsing the source code. I found that replacing List using Vector could bring a significant improvement.

At first, we need to know how ThreadScope processing event logs. After the user selects an eventlog file in the dialog window, ThreadScope starts reading the event log using the readEventLogFromFile from the ghc-events package to load the whole file and parse it into a sequence of Event data. ThreadScope will sort those events by their cap field and partition events by their cap, then build views for UI using mkDurationTree, mkEventTree, and mkSparkTree.

The processing on events is a serials of map, filter, groupBy and sort operations. For a list that contains millions of events, the operation cannot be every memory efficient and the performance won’t be very good as well. Therefore more efficient arrays should be used.

When the user drags the timeline bar, the state of timeline will be updated, and corresponding parts of event information will be shown in the current view. All events will always be kept in memory not matter what time range the user have selected on timeline bar. This is the root cause that ThreadScope cannot handle large event logs within limit memory.

List (or []) is the most commonly used container to represent sequential data in Haskell. The List is implemented using the Linked List data structure. Its implementation has been heavily optimized and the performance could even beat Linked List implemented using C! But the overhead is also obvious that many there are more extra field and more indirections in List, compared to Vector, especially when meets a large amount of elements.

From the last section, we know that ThreadScope needs to do many mapping, filtering, grouping by, and even sorting operations on the event logs. It is data intensive. Under such settings, the List is not a good choice. A compact data structure could save a lot of memory and bump up the speed. Then I tried with the efficient array package vector.

ThreadScope uses the ghc-events package to read events from eventlog file and parse the serialized bytes to structured data. ghc-events returns events as [Event] thus, the first task is returning Vector Event, rather than [Event], in ghc-events. The work is straight forward and can be found in 2067776d64:

• Update the type signatures to use Vector
• Replacing operations on List with its counterpart in the vector package
• Resolve all compiler errors (the powerful type system of Haskell makes the refactor job so easy)

The main event processing logic is in ThreadScope, in Events/ReadEvents.hs. Thanks to the power type checking of GHC again, now the task is to eliminate all type mismatch errors after upgrade to the vector version of ghc-events. By replacing the map/reduce/groupBy/sort on List with the same thing from the vector package, we made a ThreadScope that backed by the more efficient array Vector without many difficulties. The work can be found in 1ce3cde310.

## Comparison and Conclusion

From the experiment on the prototype with commit 2067776d64 and 1ce3cde310, for an eventlog file of size about 50MB, containing about 2,000,000 event records, using Vector can reduce the memory usage from about 1.3 GB to 800 MB, that is a great win!

The implementation is very preliminary and can be improved, indicates that there is still a chance to optimize ThreadScope by using a more suitable data structure. However, just doing that is not enough, since for eventlog file with GBs size, keeping all event records all the time is a bad idea and the correct fix for it must be partial loading, processing and rendering, and that will be the key of this GSoC project.