Jonas Jensen 5fd425ae95 C++: fix IRBlock::backEdgeSuccessor performance
The `IRBlock::backEdgeSuccessor` predicate, in its three copies, had
become slow:

    6:IRBlock::Cached::backEdgeSuccessor#fff ...... 1m1s
    7:IRBlock::Cached::backEdgeSuccessor#2#fff .... 52.3s
    8:IRBlock::Cached::backEdgeSuccessor#3#fff .... 26.4s

The slow part was finding all the nodes involved in cycles in the
`forwardEdgeRaw` graph. This was done with `forwardEdgeRaw+(pred, pred)`,
but that got compiled into a materialization of `forwardEdgeRaw+`, which
is a huge relation with 1,816,752,107 rows on Wireshark:

    (1474s) Starting to evaluate predicate IRBlock::Cached::backEdgeSuccessor#3#fff
    (1501s) Tuple counts:
    0          ~0%     {2} r1 = SELECT #IRBlock::Cached::forwardEdgeRaw#3#ffPlus ON FIELDS #IRBlock::Cached::forwardEdgeRaw#3#ffPlus.<0>=#IRBlock::Cached::forwardEdgeRaw#3#ffPlus.<1>
    0          ~0%     {1} r2 = SCAN r1 OUTPUT FIELDS {r1.<0>}
    0          ~0%     {3} r3 = JOIN r2 WITH IRBlock::Cached::blockSuccessor#6#fff ON r2.<0>=IRBlock::Cached::blockSuccessor#6#fff.<0> OUTPUT FIELDS {r2.<0>,IRBlock::Cached::blockSuccessor#6#fff.<1>,IRBlock::Cached::blockSuccessor#6#fff.<2>}
    12411      ~7%     {3} r4 = IRBlock::Cached::backEdgeSuccessorRaw#3#fff \/ r3
                       return r4
    (1501s)  >>> Relation IRBlock::Cached::backEdgeSuccessor#3#fff: 12411 rows using 0 MB

The problem is the `SELECT`. It's fast to join on a fastTC result once
we know what we're looking for, so this fix materializes the identity
relation on `IRBlock` and joins with that so the fastTC ends up on the
RHS of a join, where it's fast. I had to introduce a helper predicate
because even with `noopt` I couldn't get `pred = pred2` to come _before_
`forwardEdgeRaw+(pred, pred2)`. The predicate now takes less than a
second to evaluate:

    (539s) Starting to evaluate predicate IRBlock::Cached::backEdgeSuccessor#fff
    (539s)  >>> Relation IRBlock::Cached::blockImmediatelyDominates#ff: 574677 rows using 0 MB
    (539s) 	 ... created with 574677 rows and 2 columns.
    (539s) Tuple counts:
    702445     ~1%     {2} r1 = SELECT IRBlock::Cached::blockIdentity#ff ON FIELDS IRBlock::Cached::blockIdentity#ff.<0>=IRBlock::Cached::blockIdentity#ff.<1>
    702445     ~1%     {2} r2 = SCAN r1 OUTPUT FIELDS {r1.<0>,r1.<0>}
    0          ~0%     {1} r3 = JOIN r2 WITH #IRBlock::Cached::forwardEdgeRaw#ffPlus ON r2.<0>=#IRBlock::Cached::forwardEdgeRaw#ffPlus.<0> AND r2.<1>=#IRBlock::Cached::forwardEdgeRaw#ffPlus.<1> OUTPUT FIELDS {r2.<0>}
    0          ~0%     {3} r4 = JOIN r3 WITH IRBlock::Cached::blockSuccessor#2#fff ON r3.<0>=IRBlock::Cached::blockSuccessor#2#fff.<0> OUTPUT FIELDS {r3.<0>,IRBlock::Cached::blockSuccessor#2#fff.<1>,IRBlock::Cached::blockSuccessor#2#fff.<2>}
    20487      ~0%     {3} r5 = IRBlock::Cached::backEdgeSuccessorRaw#fff \/ r4
                       return r5
    (539s)  >>> Relation IRBlock::Cached::backEdgeSuccessor#fff: 20487 rows using 0 MB
2019-04-29 15:44:50 +02:00
2019-03-12 11:28:22 -07:00
2019-03-13 07:54:44 +00:00
2018-09-23 16:24:31 -07:00
2019-03-14 11:04:03 -07:00
2018-08-07 12:19:02 +01:00

Semmle QL

This open source repository contains the standard QL libraries and queries that power LGTM, and the other products that Semmle makes available to its customers worldwide.

How do I learn QL and run queries?

There is extensive documentation on getting started with writing QL. You can use the interactive query console on LGTM.com or the QL for Eclipse plugin to try out your queries on any open-source project that's currently being analyzed.

Contributing

We welcome contributions to our standard library and standard checks. Do you have an idea for a new check, or how to improve an existing query? Then please go ahead and open a pull request! Before you do, though, please take the time to read our contributing guidelines and QL style guide.

License

The QL queries in this repository are licensed under Apache License 2.0 by Semmle.

Description
CodeQL: the libraries and queries that power security researchers around the world, as well as code scanning in GitHub Advanced Security
Readme MIT 19 GiB
Languages
CodeQL 32.3%
Kotlin 27.4%
C# 17.1%
Java 7.7%
Python 4.6%
Other 10.7%