When executing internal queries, it is important that the developer
will decide if to cache the query internally or not since internal
queries are cached indefinitely. Also important is that the programmer
will be aware if caching is going to happen or not.
The code contained two "groups" of `query_processor::execute_internal`,
one group has caching by default and the other doesn't.
Here we add overloads to eliminate default values for caching behaviour,
forcing an explicit parameter for the caching values.
All the call sites were changed to reflect the original caching default
that was there.
Signed-off-by: Eliran Sinvani <eliransin@scylladb.com>
`execute_internal` has a parameter to indicate if caching a prepared
statement is needed for a specific call. However this parameter was a
boolean so it was easy to miss it's meaning in the various call sites.
This replaces the parameter type to a more verbose one so it is clear
from the call site what decision was made.
Instead of lengthy blurbs, switch to single-line, machine-readable
standardized (https://spdx.dev) license identifiers. The Linux kernel
switched long ago, so there is strong precedent.
Three cases are handled: AGPL-only, Apache-only, and dual licensed.
For the latter case, I chose (AGPL-3.0-or-later and Apache-2.0),
reasoning that our changes are extensive enough to apply our license.
The changes we applied mechanically with a script, except to
licenses/README.md.
Closes#9937
storage_proxy.hh is huge and includes many headers itself, so
remove its inclusions from headers and re-add smaller headers
where needed (and storage_proxy.hh itself in source files that
need it).
Ref #1.
Timeout config is now stored in each connection, so there's no point
in tracking it inside each query as well. This patch removes
timeout_config from query_options and follows by removing now
unnecessary parameters of many functions and constructors.
All internal execution always uses query text as a key in the
cache of internal prepared statements. There is no need
to publish API for executing an internal prepared statement object.
The folded execute_internal() calls an internal prepare() and then
internal execute().
execute_internal(cache=true) does exactly that.
* seastar d59fcef...b924495 (2):
> build: Fix protobuf generation rules
> Merge "Restructure files" from Jesse
Includes fixup patch from Jesse:
"
Update Seastar `#include`s to reflect restructure
All Seastar header files are now prefixed with "seastar" and the
configure script reflects the new locations of files.
Signed-off-by: Jesse Haber-Kucharsky <jhaberku@scylladb.com>
Message-Id: <5d22d964a7735696fb6bb7606ed88f35dde31413.1542731639.git.jhaberku@scylladb.com>
"
Since storage_proxy provides access to the entire cluster, a local shard
reference is sufficient. Adjust query_processor to store a reference to
just the local shard, rather than a seastar::sharded<storage_proxy> and
adjust callers.
This simplifies the code a little.
Message-Id: <20180415142656.25370-3-avi@scylladb.com>
Having a varadic parameter being used in implicit sprint is not
very readable + makes it less intuitive when suddenly system keyspace
becomes more than one -> multiple sprints in the chain -> more confusion
or more execution paths.
Its not that horrible with some spread out sprint:s
We use boost::any to convert to and from database values (stored in
serlialized form) and native C++ values. boost::any captures information
about the data type (how to copy/move/delete etc.) and stores it inside
the boost::any instance. We later retrieve the real value using
boost::any_cast.
However, data_value (which has a boost::any member) already has type
information as a data_type instance. By teaching data_type intances about
the corresponding native type, we can elimiante the use of boost::any.
While boost::any is evil and eliminating it improves efficiency somewhat,
the real goal is growing native type support in data_type. We will use that
later to store native types in the cache, enabling O(log n) access to
collections, O(1) access to tuples, and more efficient large blob support.
