Intersect¶

An intersect is a representation of all rows that appear in both of a pair of specified data sets (tables or views). Intersects on collections are not supported.

An intersect is performed via the /create/union endpoint using the intersect or intersect_all mode:

Intersect -- all unique rows that exist in both specified data sets
Intersect All -- all rows (including duplicates) that exist in both specified data sets

You can only perform an intersect 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 intersect creates a separate in-memory table containing the results. Intersects can be persisted (like tables) using the persist option.

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

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

Performing an Intersect¶

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

the name of the in-memory table to be created
the list of the two member data sets to be used in the intersect operation
the list of columns from each of the given data sets to be used in the intersect operation
the list of column names to be output to the resulting in-memory table
the intersect mode specified in the options input parameter

Example¶

In Python, an intersect between given tables lunch_menu and dinner_menu would look like:

gpudb.create_union(
  table_name = "lunch_intersect_dinner",
  table_names = ["lunch", "dinner"],
  input_column_names = [
    ["food_name", "category", "price"],
    ["food_name", "category", "price"]
  ],
  output_column_names = ["food_name", "category", "price"],
  options = {"mode":"intersect"}
)

The results from the above call would contain only the menu items (excluding duplicates) found in the extracted columns from both lunch_menu and dinner_menu. The result would match what would be produced by the SQL:

SELECT
  food_name,
  category,
  price
FROM
  lunch_menu

INTERSECT

SELECT
  food_name,
  category,
  price
FROM
  dinner_menu

Note

Because the example includes price, if you had two of the same items that were priced differently for lunch and dinner, the item would appear twice in the resulting intersect view because the rows would not be exact duplicates of each other.

Operating on an Intersect¶

Examples¶

A Python example filter on the results produced in the Performing an Intersect section for sandwiches available in both menus:

gpudb.filter(
  table_name = "lunch_intersect_dinner",
  view_name = "sandwiches",
  expression = "category = sandwich"
)

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

In Python, to aggregate the average, max, and minimum price across the two menus:

gpudb.aggregate_statistics(
  table_name = "lunch_intersect_dinner",
  column_name = "price",
  stats = "mean, min, max"
)

To retrieve records from the intersect results in Python:

gpudb.get_records(
    table_name = "lunch_intersect_dinner",
    offset = 0,
    limit = 25,
    encoding = "json"
)

Limitations¶

Performing an intersect 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 intersect replicated and non-replicated data sets
If attempting to intersect sharded data sets, all data sets have to be sharded similarly (if all data is not on the same processing node, the intersect can't be calculated properly)
The result of an intersect operation does not get updated if source data set(s) are updated
The results of an intersect operation has no permanence by default because it is not protected and will expire after the default TTL setting
The input_column_name parameter vector size needs to match the number of data sets listed, i.e. if you want to intersect a data set to 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.
Store-only columns and non-charN string column types cannot have intersect operations applied to them

Table Of Contents