It’s time to summarize current progress during the first round of GSoC 2019. I have finished a tool to indexing event logs into SQLite at a relative acceptable speed and worked out a prototype that can leverage SQLite to reduce the burden of memory management for large eventlog files, achieving the goal of the first round in my original proposal.

Fixing the Parser of ghc-events

There was a long-standing bug in ghc-events that this package cannot handle the par_balanced_copied field of the EVENT_GC_STATS_GHC event. This field was added in ghc-8.4.1, that means ghc-events and ThreadScope has been broken for every version of ghc compiler since 8.4.1.

The fix for the bug is trivial after understanding how mkEventTypeParsers works: adding another FixedSizeParser for EVENT_GC_STATS_GHC event that consumes 58 bits, and declare a new Maybe field in GCStatsGHC to contain the newly added par_balanced_copied value, and when the decoder meets an eventlog file that claims the EVENT_GC_STATS_GHC event has 58 bits, the new parser will be chose correctly.

(FixedSizeParser EVENT_GC_STATS_GHC (sz_capset + 2 + 5*8 + 4 + 8) (do -- (heap_capset, generation, copied_bytes, slop_bytes, frag_bytes, par_n_threads, par_max_copied, par_tot_copied, par_balanced_copied) heapCapset <- get gen <- get :: Get Word16 copied <- get :: Get Word64 slop <- get :: Get Word64 frag <- get :: Get Word64 parNThreads <- get :: Get Word32 parMaxCopied <- get :: Get Word64 parTotCopied <- get :: Get Word64 parBalancedCopied <- get :: Get Word64 return GCStatsGHC{ gen = fromIntegral gen , parNThreads = fromIntegral parNThreads , parBalancedCopied = Just parBalancedCopied , ..} )),

This patch has been organized as a pull request (ghc-events#47) and has been merged by the maintainer. After the fix, a new version of ghc-events (v0.9.0) has been released.

SQLite and ghc-events

In the proposal, we have a plan that leveraging the SQLite to managing events for us and ThreadScope can read parts of events from SQLite in a specified order, then build the view for users. Currently, I have resolved the challenge of indexing huge eventlog file into the SQLite database at a reasonable speed and have worked out a prototype of ThreadScope that loading events from SQLite. This part of work can be split into two subtasks: indexing event logs and query from SQLite. The former has been organized into a separate repository ghc-events-sqlite. The later has been pushed to my fork but hasn’t been submitted to the upstream repository yet.

The Performance of SQLite

In the beginning, the performance of SQLite is not very promising. Our preliminary test shows that it requires about 2 minutes to indexing about 2,000,000 event logs into a temporary SQLite database in ramdisk. However, the size of the eventlog file is only about 50MB, and the performance is even worse than loading the whole eventlog into ThreadScope!

After detailed debugging and experiments, I found that the speed of indexing could be optimized by tweaking the settings of ThreadScope database. After adjusting the parameter journal_mode as MEMORY and turning synchronous OFF, now only about 4 seconds is used to indexing a 50-MB eventlog to SQLite DB. Compared the previous result (2 minutes), the performance of indexing is acceptable now.

Query Events from SQLite

In the previous implementation, a list of events will be sorted and partitioned with their cap property and will be displayed in GUI along with their timestamps. When querying events from SQLite, we can leverage the database engine to perform sorting and return the sorted result set directly.

I set up an index in SQLite when creating the table, indexing on the cap and timestamp property.

DB . execute_ conn "CREATE TABLE evts (id INTEGER PRIMARY KEY, timestamp INTEGER, cap INTEGER, etype INTEGER, einfo TEXT);" DB . execute_ conn "CREATE INDEX evts_cap_timestamp ON evts (cap, timestamp);"

In ThreadScope, when we need to load events on a given capability within the given range of time, we need to do something like

DB . query conn "select timestamp, etype, einfo from evts where cap = ? and timestamp >= ? and timestamp <= ? ORDER by timestamp ASC" ( cap , start_ts , end_ts )

The result set is guaranteed sorted by the timestamp field, and there is no need sorting the List (or Vector ) again in Haskell code, thus reduce the memory usage. My experiments shows that when query events on a given capability, the index could reduce the time from about 0.5 seconds to about 0.15 seconds. By using the EXPLAIN QUERY PLAN query I can confirm that the index is used when executing the event query.

Put Things Together

There is already a prototype that loading events from the SQLite database and build the view for users, but there are still many problems with the current design. The 0.1 second that consumed by the query is still much slower, and the user can feel a pause when drag the timeline bar, compared with the original design that keeping all event records in memory.

The view is still incorrect in this prototype. The biggest challenge is about how to build a partial view on GUI correctly. Many kinds of events, like ThreadStart and ThreadStop , need to be grouped as pairs when displaying them on GUI. However, when constructing the view with events that within the given range directly, such context may be lost because of the corresponding start or stop lies out of the selected time range.

Plan for the Next Round

My work during the first round shows that our SQLite plan is promising to manage event logs and could reducing the burden like partial file reading (and parsing) as well as memory management. Basically, I have achieved the goal of the first round development in my proposal.

I will address the issue of incorrect partial view during the second round development. Another challenge is that we still need to improve the performance of querying from SQLite. Querying events for a time span within about 0.15 seconds sounds promising, but we still could feel a significant pause when dragging the timeline bar to change the view. Something like caching may help, and the settings of SQLite may need more careful tweaks.