Overview
The concept of tables is at the heart of Kinetica interactions. A table
is a data container associated with a specific type schema
(set of columns & types), much like tables in other database platforms. When
querying a table, the result set will also have a single type schema
associated with it.
Tables may exist either as top-level objects or as members of
collections.
Distribution
Table data can be distributed across the Kinetica cluster
using one of two schemes: sharding & replication. Within the sharding
scheme, data can be distributed either by key or
randomly in the absence of a
shard key.
Sharding
Sharding is the distribution of table data by hashing a
particular value for each record, and by that hash, determining on which
Kinetica cluster node the record will reside.
The benefit of sharding is that it allows distributed queries to be run
against a given data set, with each node responsible for managing its portion of
the overall data set, while only storing each record once across the cluster.
The parallelism inherent in the distributed queries allows for the query
performance to scale linearly with the addition of each cluster node.
Another benefit of sharding is that it increases the performance of any
aggregation run against the table where the aggregation group includes all of
the shard key columns.
A limitation of sharding is that two sharded data sets can only be
joined together if they are sharded in the same way, so that
their corresponding records will all reside on the same nodes. Given that, in a
typical database model, each pair of related data sets is associated by a
different key, a single query may only be able to join together at most two
sharded data sets along their relation.
Since sharding maximizes data storage & query efficiency, it is recommended
that the largest table in the system (e.g. the fact table in a data warehouse)
be sharded. The second largest table against which that table is joined
should also be sharded along the join relation (e.g. the column on each side
of the foreign key relationship).
Given that sharding allows for greater-performant aggregations, it is also
recommended that any large tables subject to frequent aggregation queries also
be sharded on the grouping columns.
For example, if the largest joined tables in a system were customer &
order, and there was a foreign key relationship between the customer_id
column of the order table & the id column of the customer table,
the order table should be sharded on customer_id and the
customer table should be sharded on id.
If, for example, there were also an order_history table, which was
frequently aggregated on store_id, it could be sharded on store_id to
increase the performance of those queries.
Specifying a shard key requires that you create a type with at least one
column that has the shard_key property when calling the
/create/type endpoint. See Shard Keys for details.
For example, in Python:
# Create a column list, annotating the intended shard key column with
# shard_key
columns = [
[ "id", gpudb.GPUdbRecordColumn._ColumnType.INT, gpudb.GPUdbColumnProperty.SHARD_KEY ],
[ "name", gpudb.GPUdbRecordColumn._ColumnType.STRING, gpudb.GPUdbColumnProperty.CHAR64 ]
]
# Create a sharded table using the column list
gpudb.GPUdbTable(
columns,
name = "sharded_table",
db = h_db
)
Random Sharding
Random sharding is the distribution of table data by randomly
selecting a shard for each batch of records during ingestion. Since all of
the records from each batch will go to the selected shard, it is important to
configure the batch size appropriately to ensure an even distribution of records
across the cluster over the entire ingestion process.
Random sharding has the same benefit as sharding with respect to distributed
query parallelism and storage minimization. It lacks, however, the benefit of
greater-performant aggregations over the shard key, as the randomly-sharded
data set lacks one.
A limitation of random sharding is that a randomly-sharded data set can only
be joined with replicated data sets--it cannot be joined with
other sharded or randomly-sharded data sets.
Given that random sharding maximizes data storage & query efficiency in the
same way that sharding does, it is recommended that large tables in the
system that are not good candidates for sharding with respect
to joins & aggregations be randomly-sharded.
For example, if a large table in a system would only ever be joined with
replicated tables and would never have aggregations run against it, the
table would be a good candidate for random sharding.
Random sharding is the default distribution technique applied to tables that
meet the following criteria:
- No primary key specified
- No shard key specified
- Not specified as replicated
For example, in Python:
# Create a column list
columns = [
[ "id", gpudb.GPUdbRecordColumn._ColumnType.INT ],
[ "name", gpudb.GPUdbRecordColumn._ColumnType.STRING, gpudb.GPUdbColumnProperty.CHAR64 ]
]
# Create a randomly sharded table using the column list
gpudb.GPUdbTable(
columns,
name = "randomly_sharded_table",
db = h_db
)
Replication
Replication is the distribution of table data by locating its
entire data set on every Kinetica cluster node simultaneously, instead of
being distributed across them.
The benefit of replication is that it allows data sets to be
joined together when those data sets are not
sharded on the columns being associated.
Normally, joining two data sets together requires them being joined on their
shard keys so that the joining of the two data sets can
occur locally on each processor shard. Since replicated data sets exist on
all shards, they appear local to any other data set and can be joined on each
shard as if they were local. The result sets from each shard are then
accumulated into one and returned as the final result set.
Since replicated data sets exist in their entirety on all processors, it is
recommended that they be relatively small. For example, a table containing
one gigabyte of data, when replicated across a 10-node cluster will occupy 10
gigabytes of cluster memory overall.
A table is specified as replicated at creation time, by calling the
/create/table endpoint with the is_replicated option set to
true.
For example, in Python:
# Create a column list
columns = [
[ "id", gpudb.GPUdbRecordColumn._ColumnType.INT ],
[ "name", gpudb.GPUdbRecordColumn._ColumnType.STRING, gpudb.GPUdbColumnProperty.CHAR64 ]
]
# Create a replicated table using the column list and the replication option
gpudb.GPUdbTable(
columns,
name = "replicated_table",
db = h_db,
options = {'is_replicated': 'true'}
)
Primary Keys
Primary key is a designation that can be applied to one or more columns in a
table. A primary key composed of multiple columns is known as
a composite primary key.
Purpose
The primary key is used to ensure the uniqueness of the data contained in the
keyed column(s).
The uniqueness constraint is enforced upon insert in two ways:
- If a record to be inserted has key values that match those of an already
existing record in the target table, the new record’s values will either:
- Overwrite the existing record’s values, if the
update_on_existing_pk
option is set to true
- Be ignored (the new record effectively discarded), if the
update_on_existing_pk option is set to false or not set
- If two or more records within a given batch insert have the same key values,
with respect to the primary key of the target table, the entire batch of
records to insert is rejected
Designation
By default, a table has no primary key. One must be explicitly designated
in the creation of the type schema associated with the
table.
Only one primary key can exist per table.
The primary key cannot exceed 320 bytes total. With
composite primary keys, the total size of all columns composing the key must
not exceed 320 bytes. For instance, the maximum number of integer columns
(4 bytes each) that can compose a primary key is 80. Unlimited-width
strings count 8 bytes each toward this total.
Note
Store-only and WKT
columns cannot be used as all or part of a primary key. Since
byte columns are store-only, they may not be part of a
primary key.
Relation to Shard Key
The primary key for a table not created as replicated
becomes its implicit shard key, used for distributing its
records across processors. Replicated tables, by definition, are not
sharded and will necessarily have no shard key, implicit or otherwise.
This implicit shard key for non-replicated tables can only be overridden
when the primary key is a composite primary key and one or more of the
composite primary key columns is explicitly designated as the shard key.
Relation to Foreign Key
The primary key designation enables the column(s) to serve as the target of a
foreign key. Note that if the primary key is a
composite primary key, the foreign key must also be a
composite foreign key, relating the columns from the source table to all
columns in the primary key.
Index
The primary key designation applies a
primary key index to the primary key columns.
Creation
For syntax and an example of creating a primary key in SQL, see
CREATE TABLE.
Creating a table with a primary key requires two steps. First, mark the
intended primary key column(s) with the primary_key property, and then
create a table with those columns.
For example, to create a table with a primary key on id, in Python:
# Create a column list, annotating the intended primary key column with
# primary_key
columns = [
[ "id", gpudb.GPUdbRecordColumn._ColumnType.INT, gpudb.GPUdbColumnProperty.PRIMARY_KEY ],
[ "name", gpudb.GPUdbRecordColumn._ColumnType.STRING, gpudb.GPUdbColumnProperty.CHAR64 ]
]
# Create a table with a primary key using the column list
gpudb.GPUdbTable(
columns,
name = "primary_key_table",
db = h_db
)
To create a table with a composite primary key on id_a & id_b:
# Create a column list, annotating the intended primary key columns with
# primary_key
columns = [
[ "id_a", gpudb.GPUdbRecordColumn._ColumnType.INT, gpudb.GPUdbColumnProperty.PRIMARY_KEY ],
[ "id_b", gpudb.GPUdbRecordColumn._ColumnType.INT, gpudb.GPUdbColumnProperty.PRIMARY_KEY ],
[ "name", gpudb.GPUdbRecordColumn._ColumnType.STRING, gpudb.GPUdbColumnProperty.CHAR64 ]
]
# Create a table with a composite primary key using the column list
gpudb.GPUdbTable(
columns,
name = "composite_primary_key_table",
db = h_db
)
Shard Keys
Shard key is a designation that can be applied to one or more columns in a
table. A shard key composed of multiple columns is known as a
composite shard key.
Purpose
The shard key is used in distributing records across processors. This
distribution allows for processing of queries against a
sharded table to be performed in parallel. In the absence of
a shard key, for non-replicated tables, a hash is computed for each record
and serves as the key by which the associated record is distributed to its
corresponding processor, or shard.
Designation
By default, a table has no shard key. One can be explicitly designated in
the creation of the type schema associated with the
table. One is also implicitly designated when a non-replicated table is
assigned a primary key. See the next section for details.
Only one shard key can exist per table.
Note
Store-only and WKT
columns cannot be used as all or part of a shard key.
Relation to Primary Key
When a primary key is assigned to any table not created
as replicated, the primary key becomes the default
shard key. All columns involved in the primary key are used as the
shard key in the order the columns were defined in the table creation.
Note
The ordering of columns within a composite shard key is critical
when performing a join between the composite shard keys of two
tables. If two tables are both sharded on the same columns, but
the tables were created with different orderings of those columns, a
sharded join between them will not be possible.
If a primary key exists on a table, one or more of the columns composing the
primary key can be designated as the shard key; only primary key columns
may be used--columns not part of the primary key may not be designated as
shard key columns. Designating a shard key does not automatically create a
corresponding primary key with the same column(s) for a table.
Creation
For syntax and an example of creating a shard key in SQL, see
CREATE TABLE.
Creating a table with a shard key requires two steps. First, mark the
intended shard key column(s) with the shard_key property, and then create
a table with those columns.
For example, to create a table with a shard key on id, in Python:
# Create a column list, annotating the intended shard key column with
# shard_key
columns = [
[ "id", gpudb.GPUdbRecordColumn._ColumnType.INT, gpudb.GPUdbColumnProperty.SHARD_KEY ],
[ "name", gpudb.GPUdbRecordColumn._ColumnType.STRING, gpudb.GPUdbColumnProperty.CHAR64 ]
]
# Create a sharded table using the column list
gpudb.GPUdbTable(
columns,
name = "sharded_table",
db = h_db
)
To create a table with a composite shard key on id_a & id_b:
# Create a column list, annotating the intended shard key columns with
# shard_key
columns = [
[ "id_a", gpudb.GPUdbRecordColumn._ColumnType.INT, gpudb.GPUdbColumnProperty.SHARD_KEY ],
[ "id_b", gpudb.GPUdbRecordColumn._ColumnType.INT, gpudb.GPUdbColumnProperty.SHARD_KEY ],
[ "name", gpudb.GPUdbRecordColumn._ColumnType.STRING, gpudb.GPUdbColumnProperty.CHAR64 ]
]
# Create a table with a composite shard key using the column list
gpudb.GPUdbTable(
columns,
name = "composite_shard_key_table",
db = h_db
)
Lastly, to create a table with a shard key that is not the primary key (but
consists of a proper subset of the columns), with the primary key on id_a
& id_b and the shard key on id_b:
# Create a column list, annotating the intended primary key columns with
# primary_key and the intended shard key column with shard_key in addition
columns = [
[ "id_a", gpudb.GPUdbRecordColumn._ColumnType.INT, gpudb.GPUdbColumnProperty.PRIMARY_KEY ],
[ "id_b", gpudb.GPUdbRecordColumn._ColumnType.INT, gpudb.GPUdbColumnProperty.PRIMARY_KEY, gpudb.GPUdbColumnProperty.SHARD_KEY ],
[ "name", gpudb.GPUdbRecordColumn._ColumnType.STRING, gpudb.GPUdbColumnProperty.CHAR64 ]
]
# Create a table with a composite primary key and a shard key that consists
# of a proper subset of the primary key columns, using the column list
gpudb.GPUdbTable(
columns,
name = "shard_key_not_primary_key_table",
db = h_db
)
Note also that sharding applies only to non-replicated tables, and the
default /create/table distribution scheme implied in the example
above is the non-replicated one. If an attempt is made to create a table
as replicated with a column set that specifies a shard key, the request will
fail. An attempt to create a replicated table with a shard key that is the
same as the primary key (all columns marked with primary_key are also
marked with shard_key) will succeed in creating a replicated table--the
shard key designation will be ignored.
Foreign Keys
Foreign key is a designation that can be applied to one or more columns in a
source table that relate them to a matching set of
primary key columns in a target table. A foreign key
composed of multiple columns is known as a composite foreign key.
Purpose
The foreign key is a tuning option for joining tables. It acts
as a relational index from a source table to corresponding records in a target
table. As new records are inserted into the source table, the index is
updated with references to the target records associated with them. If a
foreign key is not used, the join relationships are established during query
time.
Foreign keys do not provide referential integrity checks.
Designation
By default, a table has no foreign key. One can be explicitly designated in
the creation of the type schema associated with the
table. One can also be designated after table creation via the
/alter/table endpoint.
Multiple foreign keys can exist on a source table. Multiple foreign keys
may also point to a single target table; though, since foreign key targets
must be primary keys, the foreign keys would all have to point to the
primary key of the target table.
Note
Store-only columns cannot be used as all
or part of a foreign key.
Relation to Primary Key
In order for the linkage to be made successfully, a foreign key must connect
one or more columns in a source table with the primary key column(s) of a
target table. The primary key target requirement guarantees the uniqueness
of the values in the target table columns, which is necessary for the
foreign key relation to be made.
In the case of a composite primary key on the target table, all of the
columns that compose the composite primary key must be related by the
foreign key. A foreign key cannot be made to a proper subset of the
composite primary key columns in the target table.
If both tables are sharded there is a further restriction, noted below.
Relation to Shard Key
If both the source & target tables are sharded, the foreign key must also
connect the shard key of the source table to the shard key of the target
table. The sharding requirement exists to ensure the related records of
each table being connected are located on the same processing node. If either
or both tables are replicated, the sharding requirement is alleviated, as
all of the records from a replicated table will exist on all processor nodes,
and thus, appear to be local to all records from the other table involved in
the relation.
For example, if table source_table is sharded on column A, and
table target_table has a primary key & shard key on column B,
the only possible foreign key from source_table to target_table would
connect column A to column B. No other columns from source_table
can be used as source columns and no other columns from target_table can
serve as target columns for the foreign key.
In the case where the target table has a shard key whose columns are a
proper subset of the primary key columns, the same rules apply. If a table
source_table has columns A, B, & C and is sharded on B, and
table target_table has primary key columns Y & Z and is
sharded on Z, there are two possible foreign keys from source_table
to target_table:
source_table(A, B) -> target_table(Y, Z)source_table(C, B) -> target_table(Y, Z)
In both examples, all of the primary key columns (Y & Z) of the target
table are pointed to by the foreign key, and the shard key columns (B
& Z) of the two tables are related by the foreign key.
Creation
Foreign keys can be designated at the time of table creation, using the
/create/table endpoint.
For example, in Python, to create a source table and connect the
pkt_id column of the source table to the id column of an existing
target table:
# Create a column list, annotating with shard_key the column which will be
# related to the target table's primary key (and default shard key)
columns = [
[ "id", gpudb.GPUdbRecordColumn._ColumnType.INT ],
[ "pkt_id", gpudb.GPUdbRecordColumn._ColumnType.INT, gpudb.GPUdbColumnProperty.SHARD_KEY ],
[ "name", gpudb.GPUdbRecordColumn._ColumnType.STRING, gpudb.GPUdbColumnProperty.CHAR64 ]
]
# Create a table with a foreign key to the previously-created primary key
# table, using the column list and specifying the foreign key option
gpudb.GPUdbTable(
columns,
name = "foreign_key_table",
db = h_db,
options = {
"foreign_keys":"pkt_id references primary_key_table(id)"
}
)
Foreign keys can also be added after table creation, via
/alter/table.
For example, in Python, to create a composite foreign key relating the
cpkt_id_a and cpkt_id_b columns of an existing source table to the
id_a and id_b columns of an existing target table, respectively:
# Alter the composite shard key table, adding a composite foreign key to the
# composite primary key table
h_db.alter_table(
table_name = "composite_shard_key_table",
action = "create_foreign_key",
value = "(id_a, id_b) references composite_primary_key_table(id_a, id_b)"
)
Multiple foreign keys can be specified at the same time by using a semicolon
as a delimiter. Again, in Python:
# Create a column list
columns = [
[ "id", gpudb.GPUdbRecordColumn._ColumnType.INT ],
[ "pkt_id", gpudb.GPUdbRecordColumn._ColumnType.INT ],
[ "cpkt_id_a", gpudb.GPUdbRecordColumn._ColumnType.INT ],
[ "cpkt_id_b", gpudb.GPUdbRecordColumn._ColumnType.INT ],
[ "name", gpudb.GPUdbRecordColumn._ColumnType.STRING, gpudb.GPUdbColumnProperty.CHAR64 ]
]
# Create a table with foreign keys to the previously-created primary key
# & composite primary key tables, using the column list and specifying the
# foreign key option. The table will be created as replicated, as the two
# target tables being related via foreign keys are sharded on different
# keys, and this table cannot be sharded on both of those keys.
gpudb.GPUdbTable(
columns,
name = "multiple_foreign_key_table",
db = h_db,
options = {
"foreign_keys":"""
pkt_id references primary_key_table(id);
(cpkt_id_a, cpkt_id_b) references composite_primary_key_table(id_a, id_b)
""",
"is_replicated":True
}
)
Footnotes
