OCGraph Python Library - Ocient Documentation

The ocient_graph module brings a programming model (similar to GraphX) to the System directly from using the pyocient driver. The module treats a graph as two relational tables, one for vertices (nodes) and one for edges (directed links). This module provides a composable API for graph transformations, neighborhood analytics, and iterative algorithms (e.g., Pregel, PageRank). The API validates inputs, avoids destructive changes by materializing results into new tables, supports optional indexing for performance, and follows Ocient SQL conventions. The package installs separately from pyocient and exposes a Python-native interface that mirrors the library. For details, see OCGraph Java Library.

Installation

Use pyocient for connectivity and ocient_graph for graph APIs. The graph library is a separate package that depends on pyocient. For a tutorial about installing and using pyocient, see Ocient Python Module: pyocient. Install and Import Install the ocient_graph module.

Shell

pip install ocient_graph

Import the module.

Python

from pyocient import connect
from ocient_graph import (
    subgraph,
    collect_neighbors,
    EdgeDirection,
)

Data Model Requirements

Database tables that use the OCGraph Python library must adhere to this structure. In addition to the listed requirements, tables can include other columns.

Table	Description	Requirements
Vertices table	A table with one row per vertex (node). This table typically represents the anchor for graph algorithms and transforms. Many methods join edges to vertices by the `id` column.	The table must contain the `id BIGINT NOT NULL` column definition as the unique vertex identifier.
Edges table	A table with one row per directed edge (relationship). Each row is a directed edge from a source vertex to a destination vertex.	The table must contain these column definitions: `srcid BIGINT NOT NULL`, `destid BIGINT NOT NULL`

Subgraph and Filtering

Use a subgraph or various filters to restrict a graph to relevant vertices and edges. These functions create filtered copies or masked intersections, preserving schema and optional indexes for performance.

subgraph

Creates filtered vertex and edge tables using vertex and triplet predicates, retaining only edges with endpoints that remain after vertex filtering. The function creates the requested indexes and performs best-effort cleanup in the event of failure. Syntax

Python

subgraph(
    connection,
    input_schema,
    input_vertices_table,
    input_edges_table,
    result_schema,
    result_vertices_table,
    result_edges_table,
    vertex_filter,
    edge_filter,
    [ result_vertices_indexes [ , ... ] ],
    [ result_edges_indexes [ , ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input tables.
`input_vertices_table`	str	Input vertices table (must have an `id` column).
`input_edges_table`	str	Input edges table (must have `srcid` and `destid` columns).
`result_schema`	str	A writable schema to create the result tables.
`result_vertices_table`	str	Name of the filtered vertices table to create.
`result_edges_table`	str	Name of the filtered edges table to create.
`vertex_filter`	str	SQL predicate to filter the vertices (without the `WHERE` keyword). Example: `status = 'ACTIVE' AND score > 0`
`edge_filter`	str	SQL predicate that the system evaluates in a triplet context using the aliases `a` (source vertex), `b` (edge), and `c` (destination vertex). This predicate does not require a `WHERE` keyword. Example: `b.amount > 50 AND a.region = c.region`
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.
`result_edges_indexes`	List[str]	Optional. Columns to index in the result edges table (e.g., `srcid` and `destid` columns). Specify an empty list for none.

Example Create an active customer subgraph that includes only purchases exceeding $50 where the source and destination share a region.

Python

subgraph(
    connection,
    "sales",
    "customers",
    "purchases",
    "sales",
    "customers_active",
    "purchases_active",
    "status = 'ACTIVE' AND score > 0",
    "b.amount > 50 AND a.region = c.region",
    ["id","region"],
    ["srcid","destid"],
)

filter_vertices

Creates a filtered subgraph by selecting vertices that match a predicate while retaining only edges with endpoints that are in the filtered vertex set. Syntax

Python

filter_vertices(
    connection,
    input_schema,
    input_vertices_table,
    input_edges_table,
    result_schema,
    result_vertices_table,
    result_edges_table,
    vertex_filter,
    [ result_vertices_indexes [ , ... ] ],
    [ result_edges_indexes [ , ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input tables.
`input_vertices_table`	str	Input vertices table (must have an `id` column).
`input_edges_table`	str	Input edges table (must have `srcid` and `destid` columns).
`result_schema`	str	A writable schema to create the result tables.
`result_vertices_table`	str	Name of the result vertices table. The name must not conflict with the names of input tables.
`result_edges_table`	str	Name of the edges table to create. The name must not conflict with the names of input tables.
`vertex_filter`	str	SQL predicate to filter the vertices (without the `WHERE` keyword). Example: `status = 'ACTIVE' AND score > 0`
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.
`result_edges_indexes`	List[str]	Optional. Columns to index in the result edges table (e.g., `srcid` and `destid` columns). Specify an empty list for none.

Example Filter US customers and retain edges with endpoints that remain in the filtered vertex set.

Python

filter_vertices(
    connection,
    "sales", "customers", "purchases",
    "sales", "customers_us", "purchases_us",
    "country = 'US'",
    ["id"],
    ["srcid","destid"],
)

filter_edges

Creates a filtered edges table by selecting edges that match a predicate. Syntax

Python

filter_edges(
    connection,
    input_schema,
    input_edges_table,
    result_schema,
    result_edges_table,
    edge_filter,
    [ result_edges_indexes [, ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty achema containing the input edges table.
`input_edges_table`	str	Input edges table (must have `srcid` and `destid` columns).
`result_schema`	str	A writable schema to create the result tables.
`result_edges_table`	str	Result edges table name. The name must not conflict with the names of input tables.
`edge_filter`	str	SQL predicate on edges (without the `WHERE` keyword). Example: `weight > 0.5 AND type = 'ACTIVE'`
`result_edges_indexes`	List[str]	Optional. Columns to index in the result edges table (e.g., `srcid` and `destid` columns). Specify an empty list for none.

Example This example demonstrates how to create a filtered edges table from an existing purchases edge set by keeping only edges that meet a business rule (weight > 0.5 and ACTIVE). Then, the filterEdges method indexes the result on the srcid and destid columns for faster lookups.

Python

filter_edges(
    connection,
    "sales", "purchases",
    "sales", "purchases_filtered",
    "weight > 0.5 AND type = 'ACTIVE'",
    ["srcid", "destid"],
)

mask

Creates a masked subgraph by intersecting two graphs. Vertices intersect if the vertex identifier is present in both graphs. Edges intersect when the srcid and destid values are present in both graphs. The function creates a masked subgraph from rows that intersect with each other. The function copies rows that intersect from the graph defined by the arguments input_vertices_table and input_edges_table, including any attributes. You can optionally create indexes on the result subgraph tables. Syntax

Java

mask(
    connection,
    input_schema,
    input_vertices_table,
    input_edges_table,
    other_schema,
    other_vertices_table,
    other_edges_table,
    result_schema,
    result_vertices_table,
    result_edges_table,
    [ result_vertices_indexes [ , ... ] ],
    [ result_edges_indexes [ , ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input tables.
`input_vertices_table`	str	The primary vertices table to intersect (must have an `id` column). The masked subgraph created by this function copies rows that intersect from this table.
`input_edges_table`	str	The primary edges table to intersect (must have `srcid` and `destid` columns). The masked subgraph created by this function copies rows that intersect from this table.
`other_schema`	str	Schema containing the second graph.
`other_vertices_table`	str	The second vertices table to intersect.
`other_edges_table`	str	The second edges table to intersect.
`result_schema`	str	A writable schema to create the result tables.
`result_vertices_table`	str	Name of the vertices table to create.
`result_edges_table`	str	Name of the edges table to create.
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.
`result_edges_indexes`	List[str]	Optional. Columns to index in the result edges table (e.g., `srcid` and `destid` columns). Specify an empty list for none.

Example Create a masked subgraph by intersecting two graphs. The example copies vertices and edges that are present in both graphs, along with the remaining endpoints.

Java

mask(
    connection,
    "sales", "customers", "purchases",
    "ref",   "customers_ref", "purchases_ref",
    "sales", "customers_masked", "purchases_masked",
    ["id"],
    ["srcid", "destid"],
)

Transformations

Construct new vertex or edge tables by computing derived columns, reversing direction, or aggregating duplicates. These functions do not change the original inputs. Instead, the functions materialize new results.

map_vertices

Creates a new vertices table with the identifier id and computed columns. Use the result_column_expressions argument to calculate additional columns. This function can also add indexes before inserting data. Syntax

Python

map_vertices(
    connection,
    input_schema,
    input_vertices_table,
    result_schema,
    result_vertices_table,
    result_column_expressions,
    [ result_vertices_indexes [, ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input vertices table.
`input_vertices_table`	str	Input vertices table (must have an `id` column).
`result_schema`	str	A schema to create the result vertices table.
`result_vertices_table`	str	Name of the result vertices table. The name must not conflict with the names of input tables.
`result_column_expressions`	List[str]	One or more SQL expressions defining result columns beyond `id`. Use the `AS alias_name` keyword for stable names.
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.

Example Create a new vertices table with two new columns, name_upper and is_vip, and generate indexes for the id and name_upper columns.

Python

map_vertices(
    connection,
    "sales", "customers",
    "sales", "customers_enriched",
    [
      "UPPER(name) AS name_upper",
      "CASE WHEN score > 1000 THEN true ELSE false END AS is_vip",
    ],
    ["id","name_upper"],
)

map_edges

Creates a new edges table with srcid, destid, and any additional computed columns. Expressions should refer to input edge columns by their original names, and each computed expression should include an AS alias keyword. Syntax

Python

map_edges(
    connection,
    input_schema,
    input_edges_table,
    result_schema,
    result_edges_table,
    [ result_column_expressions [, ... ] ],
    [ result_edges_indexes [, ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input edges table.
`input_edges_table`	str	Input edges table (must have `srcid` and `destid` columns).
`result_schema`	str	A schema to create the result edges table.
`result_edges_table`	str	Name of the result edges table. The name must not conflict with the names of input tables.
`result_column_expressions`	List[str]	Optional. SQL expressions for additional edge columns (besides the `srcid` and `destid` columns). Use the `AS alias_name` keyword for stable names.
`result_edges_indexes`	List[str]	Optional. Columns to index in the result edges table (e.g., `srcid` and `destid` columns). Specify an empty list for none.

Example Create a new edges table with two columns, discounted_amount and big_txn, and generate indexes for the srcid and destid columns.

Python

map_edges(
    connection,
    "sales", "purchases",
    "sales", "purchases_enriched",
    [
      "amount * 0.9 AS discounted_amount",
      "CASE WHEN amount > 100 THEN 1 ELSE 0 END AS big_txn",
    ],
    ["srcid","destid"],
)

map_triplets

Creates a new edges table with computed columns that reference a (source vertex), b (edge), and c (destination vertex). The output automatically includes b.srcid and b.destid columns. Syntax

Python

map_triplets(
    connection,
    input_schema,
    input_vertices_table,
    input_edges_table,
    result_schema,
    result_edges_table,
    [ result_column_expressions [, ... ] ],
    [ result_edges_indexes [, ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input tables.
`input_vertices_table`	str	Input vertices table (must have an `id` column). Used as the `a` and `c` vertices.
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns). This function uses this argument as the `b` edge.
`result_schema`	str	A writable schema to create the result edges table.
`result_edges_table`	str	Name of the result edges table. The name must not conflict with the names of input tables.
`result_column_expressions`	List[str]	Optional. SQL expressions that can reference `a` (source vertex), `b` (edge), and `c` (destination vertex). Use the `AS alias_name` keyword for stable names.
`result_edges_indexes`	List[str]	Optional. Columns to index in the result edges table (e.g., `srcid` and `destid` columns). Specify an empty list for none.

Example Create a new triplet table from the vertices and edges tables with the amount and same_country columns, and generate indexes for the src and destid columns.

Python

map_triplets(
    connection,
    "sales", "customers", "purchases",
    "sales", "purchases_triplets",
    [
      "b.amount AS amount",
      "CASE WHEN a.country = c.country THEN 1 ELSE 0 END AS same_country",
    ],
    ["srcid","destid"],
)

reverse_edges

Creates a new edges table with the srcid and destid columns reversed, preserving other columns. Use this function to traverse a graph in the opposite direction. Syntax

Python

reverse_edges(
    connection,
    input_schema,
    input_edges_table,
    result_schema,
    result_edges_table
    [ result_edges_indexes [, ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input edges table.
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A writable schema to create the reversed edges table.
`result_edges_table`	str	Name of the reversed edges table to create. The name must not conflict with the names of input tables.
`result_edges_indexes`	List[str]	Optional. Columns to index in the result edges table (e.g., `srcid` and `destid` columns). Specify an empty list for none.

Example Transform edge direction by reversing the srcid and destid columns. The example also creates indexes for these columns.

Python

reverse_edges(
    connection,
    "sales", "purchases",
    "sales", "purchases_reversed",
    ["srcid","destid"],
)

group_edges

Groups duplicate rows of the srcid and destid columns, producing one row for each unique pair of values in a new edges table. This function performs aggregations based on one or more SQL expressions. Syntax

Python

group_edges(
    connection,
    input_schema,
    input_edges_table,
    result_schema,
    result_edges_table,
    [ result_column_expressions [, ... ] ],
    [ result_edges_indexes [, ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input edges table.
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A writable schema to create the grouped tables.
`result_edges_table`	str	Name of the grouped edges table. The name must not conflict with the names of input tables.
`result_column_expressions`	List[str]	One or more aggregate expressions for the `srcid` and `destid` columns. For details about SQL aggregations, see Aggregate Functions. Use the `AS alias_name` keyword for stable names.
`result_edges_indexes`	List[str]	Optional. Columns to index in the result edges table (e.g., `srcid` and `destid` columns). Specify an empty list for none.

Example Create a new edge table that includes SQL aggregations for counting unique transactions txn_count and total sums total_amount. Also, this function generates indexes for the src and destid columns.

Python

group_edges(
    connection,
    "sales", "purchases",
    "sales", "purchases_grouped",
    [
      "COUNT(*) AS txn_count",
      "SUM(amount) AS total_amount",
    ],
    ["srcid","destid"],
)

Triplets

Produce triplet representations that are made of a (source vertex), b (edge), and c (destination vertex), either as a logical view or a materialized table for downstream queries.

create_triplets_view

Creates a view that combines the edge table with the source and destination vertex attributes. This view is useful for analyzing relationships without having to repeatedly join tables. The view includes these columns:

All original edge columns (including the srcid and destid columns).
All source-vertex columns except id. Source-vertex column names have the src_ prefix.
All destination-vertex columns except id. Destination-vertex column names have the dest_ prefix.

Use the create_triplets_table function instead if you want to create a materialized table with indexes instead of a view. Syntax

Python

create_triplets_view(
    connection,
    input_schema,
    input_vertices_table,
    input_edges_table,
    result_schema,
    result_triplets_view
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input vertices and edges tables.
`input_vertices_table`	str	Input vertices table (must have an `id` column).
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A schema to create the view.
`result_triplets_view`	str	Name of the triplets view to create. The name must not conflict with the names of input tables.

Example Create a triplets view to inspect edges with joined source and destination vertex attributes.

Python

create_triplets_view(
    connection,
    "sales", "customers", "purchases",
    "sales", "triplets_v",
)

create_triplets_table

Creates a materialized table that combines the edge table with the source and destination vertex attributes. This table is useful for analyzing relationships without having to repeatedly join tables. The created table includes these columns:

All original edge columns (including the srcid and destid columns).
All source-vertex columns except id. Source-vertex column names have the src_ prefix.
All destination-vertex columns except id. Destination-vertex column names have the dest_ prefix.

Use the create_triplets_view function if you want to create a view instead of a new table. Syntax

Python

create_triplets_table(
    connection,
    input_schema,
    input_vertices_table,
    input_edges_table,
    result_schema,
    result_triplets_table,
    [ result_triplets_indexes [, ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input tables.
`input_vertices_table`	str	Input vertices table (must have an `id` column).
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A schema to create the triplets table.
`result_triplets_table`	str	Name of the triplets table to create. The name must not conflict with any input names.
`result_triplets_indexes`	List[str]	Optional. Columns to index in the result table. Specify an empty list for none.

Example Create a new table for triplets. Generate indexes for the src_id and dest_id columns.

Python

create_triplets_table(
    connection,
    "sales", "customers", "purchases",
    "sales", "triplets_t",
    ["srcid","destid"],
)

Degrees

Compute degree metrics for each vertex from the edges table. These functions produce small vertex tables suitable for joins and analytics.

in_degrees

Computes how many edges point to each vertex in an edge table by counting how many times each unique destid value appears. The result table has two columns: id (the destination vertex) and in_degree (the count). Syntax

Python

in_degrees(
    connection,
    input_schema,
    input_edges_table,
    result_schema,
    result_vertices_table,
    [ result_vertices_indexes [, ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input table.
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A schema to create the in-degree table.
`result_vertices_table`	str	Name of the vertices table to create (columns include `id` and `in_degree`).
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.

Example Compute in-degrees per vertex and generate an index on the id column.

Python

in_degrees(
    connection,
    "sales", "purchases",
    "sales", "customers_in_degree",
    ["id"],
)

out_degrees

Computes how many edges originate from each vertex in an edge table by counting how many times each unique srcid value appears. The result table has two columns: id (the source vertex) and out_degree (the count). Syntax

Python

out_degrees(
    connection,
    input_schema,
    input_edges_table,
    result_schema,
    result_vertices_table,
    [ result_vertices_indexes [, ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input table.
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A schema to create the out-degree table.
`result_vertices_table`	str	Name of the vertices table to create (columns include `id` and `out_degree`).
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.

Example Compute the out-degrees count for each vertex and generate an index on the id column.

Python

out_degrees(
    connection,
    "sales", "purchases",
    "sales", "customers_out_degree",
    ["id"],
)

degrees

Computes the total degrees (in-degrees and out-degrees) for each vertex in an edge table by counting how many times each unique srcid and destid value appears. The result table has two columns: id (the destination or source vertex) and degree (the count). Syntax

Python

degrees(
    connection,
    input_schema,
    input_edges_table,
    result_schema,
    result_vertices_table,
    [ result_vertices_indexes [, ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input table.
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A schema to create the degree table.
`result_vertices_table`	str	Name of the vertices table to create (columns include `id` and `out_degree`).
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.

Example Compute total degrees for each vertex and generate an index on the id column.

Python

degrees(
    connection,
    "sales", "purchases",
    "sales", "customers_degree",
    ["id"],
)

Vertex Extraction and Joins

Build vertex sets from edges and combine vertex attributes across tables. These functions are useful for shaping vertex properties and consolidating features.

from_edges

Builds a vertices table from an edges table by extracting the unique source and destination identifiers. This function can optionally compute additional columns using SQL expressions by referencing the unique identifier as ids.id. The created table always contains the id column with one additional column per expression. Syntax

Python

from_edges(
    connection,
    input_schema,
    input_edges_table,
    result_schema,
    result_vertices_table,
    [ result_column_expressions [ , ... ] ],
    [ result_vertices_indexes [ , ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input table.
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A schema to create the result vertices table.
`result_vertices_table`	str	Name of the result vertices table (always includes `id`).
`result_column_expressions`	List[str]	Optional. Specify a list of SQL expressions that reference `ids.id` to add additional columns.
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.

Example Create a vertices table from edge endpoints and add a bucket column that assigns each vertex to one of 10 buckets. Generate an index for the id and bucket columns.

Python

from_edges(
    connection,
    "sales", "purchases",
    "sales", "customers_from_edges",
    ["ids.id % 10 AS bucket"],
    ["id","bucket"],
)

join_vertices

Merges two vertices tables by retaining every row from a primary table (input_vertices_table) and selectively updating rows that also appear in the modification table (modification_vertices_table). The merged table includes all vertices from the primary table that do not appear in the modification table. For vertices that appear in both tables, the function must include a list of expressions (resultAttributeExpressions) in the same column order for every non-identifier column in the merged result table. These SQL expressions can add computations to columns, or simply add aliases if no changes are needed. Each expression can reference columns from the primary table (using alias a) or from the modification table (using alias b). Syntax

Python

join_vertices(
    connection,
    input_schema,
    input_vertices_table,
    modification_schema,
    modification_vertices_table,
    result_schema,
    result_vertices_table,
    result_attribute_expressions [ , ... ],
    [ result_vertices_indexes [ , ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the primary input table.
`input_vertices_table`	str	Input vertices table (with the alias `a`). This table must have an `id` column.
`modification_schema`	str	A schema for the modification vertices table.
`modification_vertices_table`	str	Modification vertices table (with the alias `b`). This table must have an `id` column.
`result_schema`	str	A writable schema to create the result tables.
`result_vertices_table`	str	Name of the vertices table to create.
`result_attribute_expressions`	List[str]	A list of SQL expressions that define the non-identifier columns of the joined result. Each expression can reference the left (`input_vertices_table`) vertex as `a` and the right (`modification_vertices_table`) vertex as `b`. You must end every expression with an explicit alias using `AS alias_name` so the output column names are stable.
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.

Example Merge vertex attributes and generate indexes for the id and status columns. This example includes two SQL expressions to update the status and score columns based on the modification vertex table using the COALESCE SQL reference function.

Python

join_vertices(
    connection,
    "sales", "customers",
    "sales", "customers_updates",
    "sales", "customers_merged",
    [
      "COALESCE(b.new_status, a.status) AS status",
      "COALESCE(b.score_delta + a.score, a.score) AS score",
    ],
    ["id","status"],
)

inner_join_vertices

Performs an inner join on two vertex tables using an equality comparison a.id = b.id. The result table automatically includes the id column from the first table. The function must include a list of SQL expressions (result_attribute_expressions) in the same column order for every non-identifier column in the merged result table. These SQL expressions can add computations to columns, or simply add aliases if no changes are needed. Each expression can reference columns from the primary table using the alias a or from the modification table using the alias b. Syntax

Python

inner_join_vertices(
    connection,
    input_schema,
    input_vertices_table,
    other_schema,
    other_vertices_table,
    result_schema,
    result_vertices_table,
    result_attribute_expressions [ , ... ],
    [ result_vertices_indexes [ , ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A schema for the left vertices table.
`input_vertices_table`	str	Input vertices table for the left side of the join (must have an `id` column).
`other_schema`	str	A schema for the right vertices table.
`other_vertices_table`	str	The other vertices table for the right side of the join (must have an `id` column).
`result_schema`	str	A writable schema to create the result tables.
`result_vertices_table`	str	Name of the vertices table to create.
`result_attribute_expressions`	List[str]	A list of SQL expressions that define the non-identifier columns of the joined result. Each expression can reference the left (`input_vertices_table`) vertex as `a` and the right (`other_vertices_table`) vertex as `b`. You must end every expression with an explicit alias using `AS alias_name` to ensure the output column names are stable.
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.

Example Create an inner join between two vertex tables and generate an index on the id column.

Python

inner_join_vertices(
    connection,
    "sales", "customers",
    "sales", "profiles",
    "sales", "customers_joined",
    [
      "a.id AS id",
      "a.status AS status",
      "b.tier AS tier",
    ],
    ["id"],
)

outer_join_vertices

Performs a left outer join between two vertices tables using an equality comparison a.id = b.id. The result table includes all rows from the left table. For left-table rows that have no match in the right table, any expression that reads columns from the right table with the alias b evaluates to NULL (while expressions that only read the table with the alias a remain non-NULL as usual). The method must include a list of SQL expressions (result_attribute_expressions) in the same column order for every non-identifier column in the merged result table. These SQL expressions can add computations to columns, or simply add aliases if no changes are needed. Each expression can reference columns from the primary table using the alias a or from the modification table using the alias b. Syntax

Python

outer_join_vertices(
    connection,
    input_schema,
    input_vertices_table,
    other_schema,
    other_vertices_table,
    result_schema,
    result_vertices_table,
    result_attribute_expressions [ , ... ],
    [ result_vertices_indexes [ , ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A schema for the left vertices table.
`input_vertices_table`	str	Left vertices table (must have an `id` column).
`other_schema`	str	A schema for the right vertices table (with the alias `b`).
`other_vertices_table`	str	Right vertices table (must have an `id` column).
`result_schema`	str	A writable schema to create the result tables.
`result_vertices_table`	str	Name of the vertices table to create.
`result_attribute_expressions`	List[str]	A list of SQL expressions that define the non-identifier columns of the joined result. Each expression can reference the left (`input_vertices_table`) vertex as `a` and the right (`other_vertices_table`) vertex as `b`. You must end every expression with an explicit alias using `AS alias_name` to ensure the output column names are stable.
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.

Example Perform a left outer join on two vertices tables and generate an index on the id column.

Python

outer_join_vertices(
    connection,
    "sales", "customers",
    "sales", "profiles",
    "sales", "customers_joined",
    [
      "a.id AS id",
      "a.status AS status",
      "b.tier AS tier",
    ],
    ["id"],
)

collect_neighbors

For each vertex in a table, this function collects information on neighbors (identifier and any attributes) as an array of tuples. For a specified direction (IN, OUT, or BOTH), the function aggregates tuples representing each neighboring vertex into an array. The direction types are:

IN — Neighbors with edges pointing to the vertex (edges where destid = id).
OUT — Neighbors that the vertex points to (edges where srcid = id).
BOTH — Union of IN and OUT with neighbors from incoming (destid = id) and outgoing (srcid = id) edges.

The result table has the columns id (the vertex identifier) and neighbors (an array of tuples representing each neighbor). If an error occurs after table creation, the function drops the result table. Syntax

Python

collect_neighbors(
    connection,
    input_schema,
    input_vertices_table,
    input_edges_table,
    result_schema,
    result_table,
    direction,
    [ result_indexes [ , ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input tables.
`input_vertices_table`	str	Input vertices table (must have an `id` column).
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A writable schema to create the result tables.
`result_table`	str	Name of the neighbors collection table (with the `id` and `neighbors` columns).
`direction`	EdgeDirection	Specifies the direction of traversal. Supported values are: `IN` — Neighbors that have edges pointing to the vertex (edges where `destid = id`). For example, if an edge `5` points to `10`, then for `id=10`, neighbor `5` is included. `OUT` — Neighbors that the vertex points to (edges where `srcid = id`). For example: If an edge `5` points to `10`, then for `id=5`, neighbor `10` is included. `BOTH` — The union of `IN` and `OUT`. This traversal includes neighbors from edges pointing to `id` and edges from `id`.
`result_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.

Example Collect incoming neighbors for each vertex and generate an index on the id column. The direction argument set to IN collects neighbors pointing to id.

Python

collect_neighbors(
    connection,
    "sales", "customers", "purchases",
    "sales", "neighbors_in",
    EdgeDirection.IN,
    ["id"],
)

collect_edges

For each vertex in a table, this function collects an array of adjacent edge rows based on the specified direction. The result table has two columns: id (the vertex identifier) and edges (an array of tuples, each tuple containing all columns from the edges table for a connected edge). The direction types are:

IN — Edges pointing to the vertex (edges where destid = id).
OUT — Edges originating from the vertex (edges where srcid = id).
BOTH — Union of IN and OUT that includes edges from incoming (destid = id) and outgoing (srcid = id) directions. This direction retains duplicates.

If an error occurs after table creation, the function drops the result table. Syntax

Python

collect_edges(
    connection,
    input_schema,
    input_vertices_table,
    input_edges_table,
    result_schema,
    result_table,
    direction,
    [ result_indexes [ , ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input tables.
`input_vertices_table`	str	Input vertices table (must have an `id` column).
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A writable schema to create the result tables.
`result_table`	str	Name of the edge collection table (with the `id` and `edges` columns).
`direction`	EdgeDirection	Specifies the direction of traversal. Supported values are: `IN` — Neighbors that have edges pointing to the vertex (edges where `destid = id`). For example, if an edge `5` points to `10`, then for `id=10`, neighbor `5` is included. `OUT` — Neighbors that the vertex points to (edges where `srcid = id`). For example, if an edge `5` points to `10`, then for `id=5`, neighbor `10` is included. `BOTH` — The union of `IN` and `OUT`. This traversal includes neighbors from edges pointing to `id` and edges from `id`.
`result_indexes`	List[str]	Optional. Columns to index. Specify an empty list for none.

Example Collect outgoing edges per vertex. The example sets the direction to OUT to collect edges from id.

Python

collect_edges(
    connection,
    "sales", "customers", "purchases",
    "sales", "outgoing_edges",
    EdgeDirection.OUT,
    ["id"],
)

Algorithms

High-level graph algorithms that iterate over the graph structure to produce labels, components, or counts.

label_propagation

Executes the Label Propagation Algorithm (LPA) to assign community labels to vertices. Each vertex starts with its own identifier as its label. For a number set by the maxIterations argument, each vertex updates its label to the most frequent label among its neighbors. The algorithm determines ties by choosing the smallest label. The algorithm uses temporary tables for intermediate results and drops these tables when the process completes or if it fails. Isolated vertices retain their initial label. The final table stores id and label columns and can include indexes. Syntax

Python

label_propagation(
    connection,
    input_schema,
    input_vertices_table,
    input_edges_table,
    result_schema,
    result_vertices_table,
    max_iterations,
    result_vertices_indexes,
    [ result_vertices_indexes [ , ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input tables.
`input_vertices_table`	str	Input vertices table (must have an `id` column).
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A writable schema to create the result tables.
`result_vertices_table`	str	Name of the vertices table to create (columns include `id` and `label`).
`max_iterations`	int	Maximum number of iterations (must be `1` or greater).
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.

Example Run label propagation for 10 iterations and assign labels to vertices. Generate an index on the id column.

Python

label_propagation(
    connection,
    "sales", "customers", "purchases",
    "sales", "lpa_labels",
    10,
    ["id"],
)

connected_components [#connected_components]

Identifies the connected components of an undirected graph. This algorithm configures a Pregel computation in which each vertex initially sets its component label equal to its own identifier id. In each iteration, vertices send their component label to neighbors. Each vertex updates based on the aggregated minimum value of its current component label and any received values. The process repeats until no more updates occur. The result table maps each vertex id to its final component label. Syntax

Python

connected_components(
    connection,
    input_schema,
    input_vertices_table,
    input_edges_table,
    result_schema,
    result_vertices_table,
    [ result_vertices_indexes [ , ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input tables.
`input_vertices_table`	str	Input vertices table (must have an `id` column).
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A writable schema to create the result tables.
`result_vertices_table`	str	Name of the vertices table to create.
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.

Example Compute connected components for a maximum of 20 iterations and generate an index on the id column.

Python

connected_components(
    connection,
    "sales", "customers", "purchases",
    "sales", "components",
    20,
   ["id"],
)

strongly_connected_components

Computes strongly connected components (SCC) in a directed graph. This function runs a recursive algorithm that partitions vertices into subsets where every vertex is reachable from other vertices in the same subset. This function uses recursive partitioning. The algorithm selects a pivot (typically the minimum identifier id), computes its predecessor set (vertices that can reach the pivot), and its descendant sets (vertices reachable from the pivot). Then, the function identifies the SCC as their intersection, removes that SCC from the graph, and recurses on the remainder until all vertices have been assigned to an SCC. The output contains columns for the id and component identifiers (the minimum id in the SCC). The function creates temporary tables in the result schema to store intermediate results. This function drops these tables when the computation completes or fails. The final result table contains two columns: id (vertex identifier) and component (the minimum vertex identifier in its SCC subset). Syntax

Python

strongly_connected_components(
    connection,
    input_schema,
    input_vertices_table,
    input_edges_table,
    result_schema,
    result_vertices_table,
    [ result_vertices_indexes [ , ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input tables.
`input_vertices_table`	str	Input vertices table (must have an `id` column).
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A writable schema to create the result tables.
`result_vertices_table`	str	Name of the vertices table to create (columns include `id` and `component`).
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.

Example Compute the SCC and generate an index on the id column.

Python

strongly_connected_components(
    connection,
    "sales", "customers", "purchases",
    "sales", "scc",
    ["id"],
)

TriangleCount

TriangleCount identifies all 3-cycles (triangles) in the graph and counts how many distinct triangles each vertex participates in. The algorithm first builds a canonical, undirected edge set by ensuring srcid < destid and removing duplicates to prevent double-counting. If your input edges are already canonicalized and deduplicated, use TriangleCount.run_pre_canonicalized to skip preprocessing for faster performance. The function then counts triangles (a, b, c) where a < b < c by intersecting neighbor lists and aggregates per-vertex participation to produce a result table with the id and triangle_count columns. run syntax

Python

TriangleCount.run(
    input_schema,
    input_vertices_table,
    input_edges_table,
    result_schema,
    result_vertices_table,
    [ result_vertices_indexes [ , ... ] ],
)

run_pre_canonicalized syntax

Python

TriangleCount.run_pre_canonicalized(
    connection,
    input_schema,
    input_vertices_table,
    canonical_edges_schema,
    canonical_edges_table,
    result_schema,
    result_vertices_table,
    [ result_vertices_indexes [ , ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input tables.
`input_vertices_table`	str	Input vertices table (must have an `id` column).
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns). For `runPreCanonicalized` execution, the function assumes this table is already canonicalized (e.g., `srcid < destid`) and deduplicated.
`result_schema`	str	A writable schema to create the result tables.
`result_vertices_table`	str	Name of the vertices table to create (columns include `id` and `triangle_count`).
`result_vertices_indexes`	list[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.

Examples Count Triangles Using run Canonicalize the raw edges internally, count unique triangles, and write per-vertex triangle counts with an index on the id column.

Python

TriangleCount.run(
    connection,
    "sales",
    "customers",
    "purchases",
    "sales",
    "triangle_counts",
    ["id"],
)

Count Triangles Using run_pre_canonicalized Use a pre-canonicalized, deduplicated edge table to count triangles and write per-vertex triangle counts with an index on the id column.

Python

TriangleCount.run_pre_canonicalized(
    connection,
    "sales",
    "customers",
    "sales",
    "purchases_canonical",
    "sales",
    "triangle_counts",
    ["id"],
)

pregel

Provides a generic vertex‑centered iteration framework for custom graph algorithms, similar to the Pregel model. Each iteration updates vertex states by sending messages along edges and then aggregating these messages to compute new states. The algorithm continues iterating until it reaches convergence (no state changes or no messages produced) or a specified iteration cap. The algorithm uses multiple specified SQL expressions. Syntax

Python

pregel(
    connection,
    input_schema,
    input_vertices_table,
    input_edges_table,
    result_schema,
    result_vertices_table,
    initializer_expr,
    send_to_source_expr,
    send_to_dest_expr,
    aggregate_expr,
    updater_expr,
    max_iterations,
    [ result_vertices_indexes [ , ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input tables.
`input_vertices_table`	str	Input vertices table (must have an `id` column).
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A writable schema to create the result tables.
`result_vertices_table`	str	Name of the vertices table to create (columns include `id` and `result`).
`initializer_expr`	str	A SQL expression to compute the initial state for each vertex (e.g., `CASE WHEN type = 'seed' THEN 1.0 ELSE 0.0 END`).
`send_to_source_expr`	str	Optional. Defines the message sent to the source vertex of an edge, referencing the current vertex states `a.state` (source) and `c.state` (destination), and any edge attributes in `b`. Example: `CASE WHEN a.country = c.country THEN 1 ELSE 0 END`
`send_to_dest_expr`	str	Optional. Defines the message sent to the destination vertex of an edge, referencing the current vertex states `a.state` (source) and `c.state` (destination), and any edge attributes as `b.<edge_column>`. Example: `CASE WHEN a.country = c.country THEN 1 ELSE 0 END`
`aggregate_expr`	str	A SQL aggregation to combine messages per vertex (e.g., `SUM(msg)`). For a list of supported aggregations, see Aggregate Functions.
`updater_expr`	str	A SQL expression to compute the next state from the current state and aggregated messages. For example, this code updates the state to the minimum current state and the aggregated message (similar to the connected_components function): `LEAST(a.state, COALESCE(m.aggregated_message, a.state)) AS state`
`max_iterations`	int	Maximum number of iterations (must be either `-1` or a value of `1` or greater). If this value is `-1`, the Pregel algorithm has no limit, and it runs until convergence. A value of `0` causes the algorithm to return an error.
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.

Example Run a simple Pregel computation summing incoming edge amounts into the vertex state for 10 iterations at most, and generate an index on the id column.

Python

pregel(
    connection,
    "sales", "customers", "purchases",
    "sales", "pregel_result",
    "0 AS state",
    "b.amount",
    None,
    "SUM(msg) AS aggregated_message",
    "state + COALESCE(aggregated_message, 0) AS state",
    10,
    ["id"],
)

Paths & Ranking

These functions include the shortest-path and PageRank algorithms.

shortest_paths

Computes the shortest distance from every vertex to each set of landmark vertices using an iterative relaxation algorithm. The algorithm resembles Bellman–Ford but simultaneously handles multiple destinations. Each landmark starts at distance 0 and all others at positive infinity. On each iteration, the algorithm examines every edge and checks whether traveling through the connected neighbor would yield a shorter route to a landmark. If a shorter route exists, the algorithm updates the distance of the source vertex. The process stops when no distances improve or the algorithm reaches the maximum number of iterations. After the process finishes, the algorithm writes a result table with the srcid, destid, and distance columns. Syntax

Python

shortest_paths(
    connection,
    input_schema,
    input_vertices_table,
    input_edges_table,
    result_schema,
    result_table,
    landmarks[ , ... ],
    edge_weight_column,
    max_iterations,
    [ result_vertices_indexes [ , ... ] ]
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input tables.
`input_vertices_table`	str	Input vertices table (must have an `id` column).
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A writable schema to create the result table.
`result_table`	str	Name of the distances result table. This table has the `srcid`, `destid`, and `distance` columns.
`landmarks`	List[int]	One or more landmark vertex identifiers. This list must be non-empty and contain no NULLs.
`edge_weight_column`	Optional[str]	Optional. The name of an edge weight column. If you do not specify this argument, the default value is `1.0`.
`max_iterations`	int	Maximum number of relaxation iterations. This value must be `1` or greater.
`result_indexes`	List[str]	Optional. The list of columns to index in the result table (`srcid`, `destid`, and `distance` columns). Specify an empty list for none.

Example Compute distances from landmarks and generate indexes on the src and dest columns.

Python

shortest_paths(
    connection,
    "sales", "customers", "purchases",
    "sales", "distances",
    [1, 42],
    None,
    10,
    ["srcid","destid"],
)

static_page_rank

Computes PageRank scores for each vertex over a fixed number of iterations. The algorithm follows the standard PageRank formula with a reset probability (reset_prob) and uses common table expressions to calculate contributions from incoming edges and redistribute ranks from dangling nodes. The algorithm supports two variants:

Standard PageRank — All vertices start with rank 1.0/N, where N is the number of vertices. Specify this variant if personalizationSrcId is null.
Personalized PageRank — The specified vertex starts with a rank of 1.0, while others start with a rank of 0.0. Specify this variant if personalizationSrcId is a vertex identifier.

After running PageRank for a fixed number of iterations, the function writes a result vertices table containing all original vertex columns with a new PageRank scoring column. Syntax

Python

static_page_rank(
    connection,
    input_schema,
    input_vertices_table,
    input_edges_table,
    result_schema,
    result_vertices_table,
    num_iterations,
    reset_prob,
    [ result_vertices_indexes [ , ... ] ],
    personalization_src_id,
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input tables.
`input_vertices_table`	str	Input vertices table (must have an `id` column).
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A writable schema to create the result tables.
`result_vertices_table`	str	Name of the vertices table to create (columns include all vertex columns and a `pagerank` column).
`num_iterations`	int	Number of iterations to run. This value must be 1 or greater.
`reset_prob`	float	A value between `0.0` and `1.0` that controls the damping factor for how PageRank random surfer moves across vertices. A high value (e.g., `0.85`) puts more emphasis on link structure, encouraging the algorithm to move from each vertex to a neighbor. This value means PageRank scores tend to concentrate around well-linked regions. A lower score (e.g., `0.50`) allows the algorithm to ignore edges and instead jump to a vertex chosen from a base distribution. For standard PageRank, this base is uniform over all vertices. For personalized PageRank, the base is biased toward the specified vertex. This behavior creates more uniform scoring with less sensitivity to link topology.
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.
`personalization_src_id`	Optional[int]	Determines whether PageRank uses the standard or the personalized variant. For personalized mode, specify a vertex identifier. PageRank scoring starts with this vertex set at `1.0`, while all other vertices start at `0.0`. For standard mode, specify `None`. All vertices start with rank `1.0/N`, where `N` is the number of vertices.

Example Run fixed-iteration PageRank and generate an index on the id column. This example uses a high reset probability reset_prob of 0.85 to ensure the ranking concentrates on highly linked regions.

Python

static_page_rank(
    connection,
    "sales", "customers", "purchases",
    "sales", "pagerank_static",
    10,
    0.85,
    ["id"],
    None,
)

dynamic_page_rank

Computes PageRank scores until convergence based on a specified threshold value (tolerance). Unlike the static_page_rank function, this algorithm runs iterations until the sum of absolute differences between ranks in successive iterations is less than or equal to the tolerance value. The algorithm handles personalization similarly to static_page_rank. At each iteration, the function uses the PageRank formula, collects rank values, and redistributes them. The algorithm supports two variants:

Standard PageRank — All vertices start with rank 1.0/N, where N is the number of vertices. Specify this variant if personalizationSrcId is null.
Personalized PageRank — The specified vertex starts with a rank of 1.0, while others start with a rank of 0.0. Specify this variant if personalizationSrcId is a vertex identifier.

After running PageRank until the system reaches the tolerance threshold, the function writes a vertices table containing all the original vertex columns with a new PageRank scoring column. Syntax

Python

dynamic_page_rank(
    connection,
    input_schema,
    input_vertices_table,
    input_edges_table,
    result_schema,
    result_vertices_table,
    tolerance,
    reset_prob,
    [ result_vertices_indexes [ , ... ] ],
    personalization_src_id,
)

Argument	Data Type	Description
`connection`	pyocient.Connection	An active database connection using the `pyocient` module.
`input_schema`	str	A non-empty schema containing the input tables.
`input_vertices_table`	str	Input vertices table (must have an `id` column).
`input_edges_table`	str	Input edges table (must have the `srcid` and `destid` columns).
`result_schema`	str	A writable schema to create the result tables.
`result_vertices_table`	str	Name of the vertices table to create (columns include all vertex columns and `pagerank`).
`tolerance`	float	The convergence threshold that stops iterations when the rank changes become negligible. After each iteration, the algorithm measures the maximum change in any rank of a vertex. If that change is below the specified tolerance, the algorithm considers the computation converged and stops early. A high tolerance value (e.g., `0.001`) is good for quick exploratory runs that generate an approximate ranking. A low tolerance value (e.g., `0.000001`) generates a precise ranking, but requires a higher compute cost.
`reset_prob`	float	A value between `0.0` and `1.0` that controls the damping factor for how the PageRank random surfer moves across vertices. A high value (e.g., `0.85`) puts more emphasis on link structure, encouraging the algorithm to move from each vertex to a neighbor. The high value means PageRank scores tend to concentrate around well-linked regions. A lower score (e.g., `0.50`) allows the algorithm to ignore edges and instead jump to a vertex chosen from a base distribution. For standard PageRank, this base is uniform over all vertices. For personalized PageRank, the base is biased toward the specified vertex. This behavior creates more uniform scoring with less sensitivity to link topology.
`result_vertices_indexes`	List[str]	Optional. Columns to index in the result vertices table (e.g., `id`). Specify an empty list for none.
`personalization_src_id`	Optional[int]	Determines whether PageRank uses the standard or the personalized variant. For personalized mode, specify a vertex identifier. PageRank scoring starts with this vertex set at `1.0`, while all other vertices start at `0.0`. For standard mode, specify `None`. All vertices start with rank `1.0/N`, where `N` is the number of vertices.

Example Run dynamic PageRank to convergence and generate an index on the id column. This example uses a low tolerance value of 1.0e-6, which generates high-precision rankings but requires more computing resources.

Python

dynamic_page_rank(
    connection,
    "sales", "customers", "purchases",
    "sales", "pagerank_dynamic",
    1.0e-6,
    0.85,
    ["id"],
    None,
)

Bibliography

Pregel: A System for Large-Scale Graph Processing.” Accessed November 18, 2025. https://research.google/pubs/pregel-a-system-for-large-scale-graph-processing/. OCGraph Java Library Ocient Python Module: pyocient

​Installation

​Data Model Requirements

​Subgraph and Filtering

​subgraph

​filter_vertices

​filter_edges

​mask

​Transformations

​map_vertices

​map_edges

​map_triplets

​reverse_edges

​group_edges

​Triplets

​create_triplets_view

​create_triplets_table

​Degrees

​in_degrees

​out_degrees

​degrees

​Vertex Extraction and Joins

​from_edges

​join_vertices

​inner_join_vertices

​outer_join_vertices

​collect_neighbors

​collect_edges

​Algorithms

​label_propagation

​connected_components [#connected_components]

​strongly_connected_components

​TriangleCount

​pregel

​Paths & Ranking

​shortest_paths

​static_page_rank

​dynamic_page_rank

​Bibliography

​Related Links

Installation

Data Model Requirements

Subgraph and Filtering

subgraph

filter_vertices

filter_edges

mask

Transformations

map_vertices

map_edges

map_triplets

reverse_edges

group_edges

Triplets

create_triplets_view

create_triplets_table

Degrees

in_degrees

out_degrees

degrees

Vertex Extraction and Joins

from_edges

join_vertices

inner_join_vertices

outer_join_vertices

collect_neighbors

collect_edges

Algorithms

label_propagation

connected_components [#connected_components]

strongly_connected_components

TriangleCount

pregel

Paths & Ranking

shortest_paths

static_page_rank

dynamic_page_rank

Bibliography

Related Links