Summary:
Store accumulated results as `communication::bolt::Value`s instead of
`TypedValue`s.
Add additional overloads for `Result` and `Summary` which accept `TypedValue`s
but internally perform conversions.
Reviewers: teon.banek, mferencevic
Reviewed By: teon.banek
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D2514
Summary:
Depends on D2471
- Add pointer to storage to `InterpreterContext`
- Rename `operator()` to `Prepare`
- Use `Interpret` instead of `operator()` (`Interpret` will be removed soon)
- Remove the `in_explicit_transaction` parameter
- Remove the memory resource parameter from `Interpret`
- Remove the storage accessor parameter from `Interpret`
- Fix up tests (remove the `Interpreter` from `database_transaction_timeout`)
Reviewers: teon.banek, mferencevic
Reviewed By: teon.banek
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D2482
Summary: Make `InterpreterContext` a top level instead of a nested struct
Reviewers: teon.banek
Reviewed By: teon.banek
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D2512
Summary:
This makes Gid the same as the one in storage/v2. Before they can be
merge into one implementation, we probably want to have a similar
transition for remaining ID types.
Depends on D2346
Reviewers: mferencevic, ipaljak
Reviewed By: mferencevic
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D2347
Summary:
With a pool allocator, lookups in STL set and map are up to 50% faster.
This is probably due to contiguous memory of pooled objects, i.e. nodes
of those containers. In some cases, the lookup outperforms the SkipList.
Insertions are also faster, though not as dramatically, up to 30%. This
does make a significant difference when the STL containers are used in a
single thread as they outperform the SkipList significantly.
Reviewers: mferencevic, ipaljak
Reviewed By: mferencevic
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D2326
Summary:
This is a preparation step in case we want to have a custom allocator in
SkipList. For example, pool based allocator for SkipListNode.
Introduction of MemoryResource and removal of `calloc` has reduced the
performance a bit according to micro benchmarks. This performance hit is
not visible on benchmarks which do more concurrent operations.
Reviewers: mferencevic, mtomic
Reviewed By: mferencevic
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D2140
Summary:
Micro benchmarks show some minor variations compared to the previous
commit. Smaller cases are a bit worse while larger data cases are a bit
better.
Reviewers: mtomic, mferencevic
Reviewed By: mtomic
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D2136
Summary:
Micro benchmarks show improvements in performance of MapLiteral from 5%
to 40% depending on the size of the input. On the other hand, a sequence
of AdditionOperators behaves the same with both allocation schemes.
Reviewers: mtomic, mferencevic, msantl
Reviewed By: mtomic
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D2132
Summary:
Micro benchmarks show no change compared to global new & delete. This is
to be expected, because Unwind relies only on `std::vector` which ought
to reserve the memory in reasonable chunks.
Reviewers: mtomic, llugovic
Reviewed By: mtomic
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D2064
Summary:
Micro benchmarks show an improvement to performance of about 10%
compared to global new & delete.
Reviewers: mtomic, llugovic
Reviewed By: mtomic
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D2061
Summary:
Micro benchmarks show that MonotonicBufferResource improves performance
by a factor of 1.5.
Reviewers: mtomic, mferencevic, llugovic
Reviewed By: mtomic
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D2048
Summary:
Benchmarks show minor improvements. Perhaps it makes sense at some later date
to use another allocator for things lasting only in a single `Pull`.
Reviewers: mferencevic, mtomic, llugovic
Reviewed By: mferencevic
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D2018
Summary:
Unfortunately, the written micro benchmark only reports minor
improvements compared to default allocator. The results are in some
cases even a tiny bit worse.
Reviewers: mtomic, mferencevic, llugovic
Reviewed By: mtomic
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D2039
Summary:
According to the written benchmark, using MonotonicBufferResource yields
significant improvements to performance of Distinct. The setup fills the
database with vertices depending on the benchmark state. No edges are
created. Then we run DISTINCT on that. Since each vertex is unique, we
will store everything in the `DistinctCursor::seen_rows_`, which is
backed by a MemoryResource. This setup, on my machine, yields 10 times
better performance when run with MonotonicBufferResource.
Reviewers: mferencevic, mtomic, msantl
Reviewed By: mferencevic
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D1894
Summary:
There will be a lot of leftover files, execute the following commands inside
`src/` to remove them:
```
git clean -xf
rm -r rpc/ storage/single_node_ha/rpc/
```
Reviewers: teon.banek
Reviewed By: teon.banek
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D2011
