With a collection, setting two separate elements in a collection would
cause the second to override the first. This also applies, with much
smaller effect, to normal cells (for example, updating the same counter
twice, or issuing two updates to the same cell but with different timestamps,
via thrift).
Fix by merging the two values rather than replacing the old one.
We use bytes for many different things, and it is easy to get confused as
to what format the data is actually in.
Fix that for atomic_cell by proving wrappers. atomic_cell::one corresponds
to a bytes object holding exactly one atomic cell, and atomic_cell::view is
a bytes_view to an atomic_cell. The static functions of atomic_cell itself
are privatized to prevent the unwashed masses from using them on the wrong
objects.
Since a row entry can hold either a an atomic cell, or a collection,
depending on the schema, also introduce a variant type
atomic_cell_or_collection and allow the user to pick the type explicitly.
Internally both are stored as bytes object.
Storing cells as boost::any objects makes us use expensive
boost::any_cast to access the data. This change replaces boost::any
with bytes object which holds the value in serialized form (the same
as will be used for on-wire format).
If the cell type is atomic, you use fields accessors defined in
atomic_cell class, eg like this:
if (column.type.is_atomic()) {
if (atomic_cell::is_live(c) {
auto timestamp = atomic_cell::timestamp(c);
...
}
}
Eventually we could switch to a more officient semi-serialized form
with native byte order but I don't want to introduce it just yet for
simplicity.
Add database::shard_of() to compute the shard hosting the partition
(with a simplistic algorithm, but perhaps not too bad).
Convert non-metadata invoke_on_all() and local calls on the database
to use shard_of().
s/database/distributed<database>/ everywhere.
Use simple distribution rules: writes are broadcast, reads are local.
This causes tremendous data duplication, but will change soon.
With replication, we want the contents of the mutation to be available
to multiple replicas.
(In this context, we will replicate the mutation to all shards in the same
node, as a temporary step in sharding a node; but the issue also occurs
when replicating to other nodes).
For simplicity partition data is stored using the same object which is
used for mutations: mutation_partition. Later we can introduce a more
efficient version.
In Origin, some places in CQL3 package treat ColumnDefinition as
ColumnSpecification. Origin solves that by making ColumnDefinition
extend ColumnSpecification.
I find it much simpler to provide an external adapter, which is cached
as a field for efficiency. Another, more important, reason for this
solution is that column_specifications are passed around as
shared_ptrs and I don't want to add unnecessary indirection to
column_definition accesses just because of that, to make it inherit
from column_specification.
Regular columns may have names of arbitrary type. See
https://issues.apache.org/jira/browse/CASSANDRA-8178
Primary key columns are UTF8.
This change also does some refactoring of the schema object to make
the change easier to digest (more encapsulation).
This patch includes a helper function that executes a function for each entry
in a directory. It is future based and can include in the future, future-based
code to asychronously read, for instance, an sstable.
At the moment, it only scan all keyspaces and make sure they appear in the
keyspaces hash.
Both the database and keyspace classes gain a populate<T> factory that returns a
populated database. At this point, the names found are just listed, but not really
stored anywhere.
Signed-off-by: Glauber Costa <glommer@cloudius-systems.com>
