Summary:
GraphDbAccessor is now constructed only through GraphDb. This allows the
concrete GraphDb to instantiate a concrete GraphDbAccessor. This allows
us to use virtual calls, so that the implementation may be kept
separate. The major downside of doing things this way is heap allocation
of GraphDbAccessor. In case it turns out to be a real performance
issues, another solution with pointer to static implementation may be
used.
InsertVertexIntoRemote is now a non-member function, which reduces
coupling. It made no sense for it to be member function because it used
only the public parts of GraphDbAccessor.
Reviewers: msantl, mtomic, mferencevic
Reviewed By: msantl
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D1504
Summary:
GraphDb is refactored to become an API exposing different parts
necessary for the database to function. These different parts can have
different implementations in SingleNode or distributed Master/Server
GraphDb implementations.
Interally GraphDb is implemented using two class heirarchies. One
contains all the members and correct wiring for each situation. The
other takes care of initialization and shutdown. This architecture is
practical because it can guarantee that the initialization of the
object structure is complete, before initializing state.
Reviewers: buda, mislav.bradac, dgleich, teon.banek
Reviewed By: teon.banek
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D1093
Summary:
Union query combinator implementation consists of:
* adjustments to the AST and `cypher_main_visitor`
* enabling `QueryStripper` to parse multiple `return` statements (not stopping after first)
* symbol generation for union results
* union logical operator
* query plan generator adjustments
Reviewers: teon.banek, mislav.bradac
Reviewed By: teon.banek
Subscribers: pullbot, buda
Differential Revision: https://phabricator.memgraph.io/D1038
Summary:
Remove name from GraphDb.
Take GraphDb in query test macros instead of accessor.
Add is_accepting_transactions flag to GraphDb.
Reviewers: mislav.bradac, florijan, mferencevic
Reviewed By: mislav.bradac
Subscribers: mferencevic, pullbot
Differential Revision: https://phabricator.memgraph.io/D940
Summary: This change increases the planning time, but should reduce memory consumption.
Reviewers: florijan, mislav.bradac
Reviewed By: mislav.bradac
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D901
Summary:
- Removed BreadthFirstAtom, using EdgeAtom only with a Type enum.
- Both variable expansions (breadth and depth first) now have mandatory inner node and edge Identifiers.
- Both variable expansions use inline property filtering and support inline lambdas.
- BFS and variable expansion now have the same planning process.
- Planner modified in the following ways:
- Variable expansions support inline property filtering (two filters added to all_filters, one for inline, one for post-expand).
- Asserting against existing_edge since we don't support that anymore.
- Edge and node symbols bound after variable expansion to disallow post-expand filters to get inlined.
- Some things simplified due to different handling.
- BreadthFirstExpand logical operator merged into ExpandVariable. Two Cursor classes remain and are dynamically chosen from.
As part of planned planner refactor we should ensure that a filter is applied only once. The current implementation is very suboptimal for property filtering in variable expansions.
@buda: we will start refactoring this these days. This current planner logic is too dense and complex. It is becoming technical debt. Most of the time I spent working on this has been spent figuring the planning out, and I still needed Teon's help at times. Implementing the correct and optimal version of query execution (avoiding multiple potentially expensive filterings) was out of reach also due to tech debt.
Reviewers: buda, teon.banek
Reviewed By: teon.banek
Subscribers: pullbot, buda
Differential Revision: https://phabricator.memgraph.io/D852
Summary:
Benchmark planning and estimating indexed ScanAll. According to the benchmark,
caching speeds up the whole process of planning and estimation by a factor of
2. Most of the performance gain is in the `CostEstimator` itself, due to plenty
of calls to `VerticesCount` when estimating all of the generated plans.
Reviewers: mislav.bradac, florijan
Reviewed By: mislav.bradac
Subscribers: pullbot
Differential Revision: https://phabricator.memgraph.io/D765
