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Tables

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.

Each table is identified by a name, which must meet the following criteria:

  • Between 1 and 200 characters long
  • Alphanumeric, including spaces and these symbols: _ { } [ ] . : - ( )
  • First character is alphanumeric or an underscore
  • Unique system-wide--cannot have the same name as another table, view, or collection, regardless of whether it is top-level or a member of a collection

Column names must meet the following criteria:

  • Between 1 and 200 characters long
  • Alphanumeric, including these symbols: _ { } [ ] . :
  • First character is alphanumeric or an underscore

Using the /create/table endpoint, you can create a empty table that can later hold records.

For example, in Python, to create a simple 2-column table:


    # Create a column list
    columns = [
        [ "id", gpudb.GPUdbRecordColumn._ColumnType.INT ],
        [ "name", gpudb.GPUdbRecordColumn._ColumnType.STRING, gpudb.GPUdbColumnProperty.CHAR64 ]
    ]

    # Create a simple table using the column list
    gpudb.GPUdbTable(
        columns,
        name = "simple_table",
        db = h_db

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 160 bytes total. This means that the largest charN column that can be designated as a primary key is char128; a char256 column cannot be a primary key. With composite primary keys, the total size of all columns composing the key must not exceed 160 bytes. For instance, the maximum number of integer columns (4 bytes each) that can compose a primary key is 40. Unlimited-width strings count 8 bytes each toward this total.

Note

Store-only 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 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
        }