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:
This change introduces dumping of indices keys. During the dump process,
an internal label is assigned to each vertex and index on vertex's
internal property id is created for faster matching during edge creation.
Reviewers: teon.banek
Reviewed By: teon.banek
Subscribers: msantl, pullbot
Differential Revision: https://phabricator.memgraph.io/D2046
Summary:
Prior to this change, a huge query was returned by DumpGenerator that
dumped the entire graph. This change split the single query to multiple
queries, each dumping a single vertex/edge. For easier vertex matching
when dumping edge, an internal property id is assigned to each vertex and
removed after the whole graph is dumped.
Reviewers: teon.banek
Reviewed By: teon.banek
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D2038
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
Summary:
This API change is needed in order to propagate the memory allocation
scheme for the execution of LogicalOperator::Cursor
Depends on D1990
Reviewers: mtomic, mferencevic
Reviewed By: mtomic
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D1980
Summary:
The new distributed directory is inside the query, and mirrors the query
structure. This groups all of the distributed (query) source code
together, which should make the potential directory extraction easier.
Reviewers: mferencevic, llugovic, mtomic
Reviewed By: mferencevic
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D1923
Summary:
The tests `RelationshipPatternNoDetails` and `PatternPartBraces` in
`memgraph__unit__cypher_main_visitor` checked for the names of the anonymous
identifiers and therefore implicitly relied on the order of the traversal of the
tree.
This "bug" surfaced when Memgraph was compiled with GCC (tested on >= 6.3.0).
Reviewers: mtomic, teon.banek, mferencevic
Reviewed By: mtomic
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D1945
Summary: New tutorial for backpacking through europe
Reviewers: dsantl, msantl, buda
Reviewed By: dsantl
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D1954
Summary:
Test to check that the recovery works even if the snapshot is corrupted
in distributed.
Depends on D1930
Reviewers: vkasljevic, mferencevic
Reviewed By: mferencevic
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D1950
Summary:
Same as `UniqueLabelPropertyConstratint` except it works with multiple properties.
Because of that it is a bit more complex and slower.
Reviewers: msantl, ipaljak
Reviewed By: msantl
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D1926
Summary:
This is a bugfix for D1836. It made `SymbolTable` return references to vector
elements, which then get invalidated and weird stuff happens.
This made a `DCHECK` in `rule_based_planner.hpp` trigger, and it was noticed by
@ipaljak 2 months later. All `DCHECK`s in `rule_based_planner.hpp` are now
changed to `CHECK`s.
Also, hash function for `Symbol` was wrong, because it also took
`user_declared` field into consideration, and `==` operator doesn't do that.
Reviewers: ipaljak, teon.banek, mferencevic, msantl
Reviewed By: msantl
Subscribers: pullbot, ipaljak
Differential Revision: https://phabricator.memgraph.io/D1938
Summary:
For HA benchmarks, if one of the executables exits with a status other
than zero, the benchmark should fail.
Also, removing `LOG(INFO)`, since failing benchmarks should flag where to look.
Reviewers: ipaljak
Reviewed By: ipaljak
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D1921
Summary:
This macro benchmark measures read throughput in HA.
The test first creates a random graph with a given number of nodes
and edges. After that, it concurently performs the following query
for 10 seconds:
```
MATCH (n {id:$random_id})-[e]->(m) RETURN e, m;
```
In other words, it randomly picks a node and returns all its neighbours.
Locally measured results are as follows:
| nodes | edges | queries per second |
| 100 | 500 | 8900 |
| 1000 | 5000 | 2700 |
| 10000 | 50000 | 1200 |
Running the same test on Memgraph single node yields very similar results
(up to a few hundred queries).
Reviewers: msantl
Reviewed By: msantl
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D1916
