Except¶

An except is a representation of all unique rows in one data set (table or view) that do not appear in another. Excepts on collections are not supported. See EXCEPT for the SQL version of this operation.

An except is performed via the /create/union endpoint using the except or except_all mode:

Except -- all unique rows that exist in one data set, but not the other
Except All -- all rows (including duplicates) that exist in one data set, but not the other

You can only perform an except two data sets, and the columns between the two must have similar data types. Kinetica will cast compatible data types as depicted here.

Performing an except creates a separate memory-only table containing the results. Excepts can be persisted (like tables) using the persist option.

Note that if the source tables or views are replicated, the results of the except will also be replicated. If the included tables or views are sharded, the resulting memory-only table from the except will also be sharded; this also means that if a non-sharded table or view is included, the resulting memory-only table will also not be sharded.

Limitations on using except are discussed in further detail in the Limitations section.

Performing an Except¶

To perform an except on two data sets, the /create/union endpoint requires five parameters:

the name of the memory-only table to be created
the list of the two member data sets to be used in the except operation; the result will contain all of the elements from the first data set that don't also exist in the second one
the list of columns from each of the given data sets to be used in the except operation
the list of column names to be output to the resulting memory-only table
the except mode specified in the options input parameter

Example¶

In Python, an except between the lunch_menu table and the dinner_menu table would look like:

h_db.create_union(
    table_name = "example.lunch_minus_dinner_menu",
    table_names = ["example.lunch_menu", "example.dinner_menu"],
    input_column_names = [
        ["food_name", "category", "price"],
        ["food_name", "category", "price"]
    ],
    output_column_names = ["lunch_food_name", "category", "price"],
    options = {"mode": "except"}
)

The results from the above call would contain all menu items (excluding duplicates) found in the extracted columns from the lunch table that are not found in the extracted columns from the dinner table.

Note

Since the example includes price and all columns selected must match between the two sets for an item to be eliminated, a lunch item that is priced differently as a dinner item would still appear in the result set.

Operating on an Except¶

Examples¶

The except created in the Performing an Except section can be filtered for lunch food items that are in the sandwich category:

h_db.filter(
    table_name = "example.lunch_minus_dinner_menu",
    view_name = "example.lunch_only_sandwiches",
    expression = "category = 'sandwich'"
)

When executed against an except, the /filter endpoint produces a filtered view. A chain of these filters could be used to create more and more restrictive views from the original except operation.

To retrieve records from the except results in Python:

product_table = gpudb.GPUdbTable(
    _type = None,
    name = "example.lunch_minus_dinner_menu",
    db = h_db
)
product_table.get_records_by_column(
    [
        "lunch_food_name",
        "category",
        "price"
    ],
    options = {"order_by": "lunch_food_name"},
    print_data = True
)

Limitations¶

Performing an except between two data sets results in an entirely new data set, so be mindful of the memory usage implications.
All data sets have to be replicated or not replicated, e.g., you cannot perform an except on replicated and non-replicated data sets.
If attempting to perform an except on sharded data sets, all data sets have to be sharded similarly (if all data is not on the same processing node, the except can't be calculated properly).
The result of an except operation does not get updated if source data set(s) are updated.
The input_column_name parameter vector size needs to match the number of data sets listed, i.e. if you want to perform an except between a data set and itself, the data set will need to be listed twice in the table_names parameter.
The input_column_name parameter vectors need to be listed in the same order as their source data sets, e.g., if two data sets are listed in the table_names parameter, the first data set's columns should be listed first in the input_column_name parameter, etc.
An except cannot be performed using a data set with non-charN string fields.
Store-only columns and non-charN string column types cannot have except operations applied to them.
The result of an except is transient, by default, as it is not protected and will expire after the default TTL setting.
The result of an except is not persisted, by default, and will not survive a database restart; specifying a persist option of true will make the table permanent and not expire.

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