Overview of GFQL ================= .. image:: _static/gfql-mascot.png :alt: GFQL mascot :width: 200px :align: center New to GFQL, the open source dataframe-native graph query language? This article overviews the gaps it fills, special features like GPU accelerations, and where to go next. Why GFQL? ~~~~~~~~~~~ GFQL addresses a critical gap in the data community by providing an in-process graph query language that operates at the compute tier. This means you can: - **Graph search**: Easily and efficiently query and filter nodes and edges using a familiar syntax. - **Avoid External Infrastructure**: Avoid calls to external infrastructures and eliminate the need for extra databases. - **Leverage Existing Workflows**: Integrate with your current Python data science tools and libraries. - **Achieve High Performance**: Utilize GPU acceleration for massive speedups in graph processing. - **Simplify Graph Analytics**: Write expressive and concise graph queries in Python. Key Features ~~~~~~~~~~~~~ - **Dataframe-Native Integration**: Works directly with Pandas, cuDF, and Apache Arrow dataframes. - **High Performance**: Optimized for both CPU and GPU execution, capable of processing billions of edges. - **Ease of Use**: Install via `pip` and start querying without the need for external databases. - **Seamless Visualization**: Integrated with PyGraphistry for GPU-accelerated graph visualization. - **Flexibility**: Suitable for a wide range of applications, including cybersecurity, fraud detection, financial analysis, and more. - **Architectural Freedom**: Use GFQL with your dataframes on your local CPU/GPU, or offload to a remote GPU cluster. Installation Guide ~~~~~~~~~~~~~~~~~~~ .. toctree:: :hidden: GFQL is built into pygraphistry: .. code-block:: bash pip install graphistry Ensure you have `pandas` or `cudf` installed, depending on whether you want to run on CPU or GPU. For more information, see :doc:`../install/index` . Key GFQL Concepts ~~~~~~~~~~~~~~~~~~~~~ GFQL works on the same graphs as the rest of the PyGraphistry library. The operations run on top of the dataframe engine of your choice, with initial support for Pandas dataframes (CPU) and cuDF dataframes (GPU). - **Nodes and Edges**: Represented using dataframes, making integration with Pandas and cuDF seamless - **Cypher strings**: Write queries as Cypher strings — ``g.gfql("MATCH (n) WHERE n.score > 5 RETURN n")`` - **Native chains**: Or compose queries as Python objects — ``g.gfql([n({"score": gt(5)})])`` - **Predicates**: Apply conditions to filter nodes and edges based on their properties, reusing the optimized native operations of the underlying dataframe engine - **Same-path constraints (WHERE)**: Relate attributes across steps in a chain using `where` - **Row pipelines (`MATCH ... RETURN` style)**: Move from graph pattern matches to tabular results with `rows()`, `where_rows()`, `return_()`, `order_by()`, `group_by()`, `skip()`, and `limit()` - **Result kinds**: Some stages keep you in graph state, while row-pipeline stages and row-returning local Cypher `CALL` queries move you into row state - **GPU & CPU vectorization**: GFQL automatically leverages GPU acceleration and in-memory columnar processing for massive speedups on your queries - **Optional remote mode**: Bind to remote data or upload it quickly as Arrow, and run your same Python and GFQL queries on remote GPU resources when available Choosing Entry Points And Result Kinds ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Use the entrypoint that matches where the query executes: - **Local in-memory GFQL / Cypher-style execution**: `g.gfql([...])` or `g.gfql("MATCH ...")` runs on the current `Plottable` in pandas/cuDF. - **Remote GFQL execution**: `g.gfql_remote([...])` runs the same GFQL chains/DAGs remotely, which is useful for larger datasets and remote GPU execution. See :ref:`gfql-remote`. .. warning:: `graphistry.cypher("...")` and `g.cypher("...")` are a separate remote database Cypher path (for example, Neo4j/Neptune integrations), not the GFQL execution surface described on this page. Do not treat them as interchangeable with `g.gfql(...)` or `g.gfql_remote(...)`. GFQL pipelines also have two practical result kinds: - **Graph state**: Traversable graph results with meaningful `_nodes` and `_edges`. Matchers, graph-preserving `call(...)` transforms, `let()` / `ref()` DAG stages, local Cypher `CALL graphistry.*.write()` queries, and local Cypher `GRAPH { MATCH ... }` constructors stay in graph state. - **Row state**: Tabular results stored in `_nodes`, with `_edges` reduced to an empty placeholder frame. Row-pipeline steps like `rows()`, `with_()`, `select()`, `return_()`, `group_by()`, and row-returning local Cypher `CALL ... YIELD ... RETURN ...` queries move into row state. - A bare local Cypher procedure call without `.write()` is also row-returning. For example, `CALL graphistry.degree()` materializes the default procedure output columns into `_nodes` and clears `_edges`. If you need to enrich a graph and keep matching locally, use graph-preserving `call()` / `let()` composition or a bare local Cypher `CALL graphistry.*.write()`. The local Cypher compiler currently supports `graphistry.degree.write()` plus `graphistry.igraph..write()` and `graphistry.cugraph..write()` for algorithms exposed through `compute_igraph()` / `compute_cugraph()`, along with a curated NetworkX subset including `graphistry.nx.pagerank.write()`, `graphistry.nx.betweenness_centrality.write()`, `graphistry.nx.degree_centrality.write()`, `graphistry.nx.closeness_centrality.write()`, `graphistry.nx.eigenvector_centrality.write()`, `graphistry.nx.katz_centrality.write()`, `graphistry.nx.connected_components.write()`, `graphistry.nx.strongly_connected_components.write()`, `graphistry.nx.core_number.write()`, `graphistry.nx.hits.write()`, `graphistry.nx.edge_betweenness_centrality.write()`, and `graphistry.nx.k_core.write()`. Quick Examples ~~~~~~~~~~~~~~~ GFQL supports Cypher strings and native Python chains through the same ``g.gfql(...)`` entrypoint: **Find Nodes of a Certain Type** .. code-block:: python # Cypher string — returns a DataFrame of matching nodes nodes_df = g.gfql("MATCH (n {type: 'person'}) RETURN n")._nodes # Equivalent native chain from graphistry import n nodes_df = g.gfql([ n({"type": "person"}) ])._nodes **Extract a Subgraph** .. code-block:: python # Cypher string — GRAPH { } returns a subgraph with ._nodes and ._edges g2 = g.gfql( "GRAPH { " "MATCH (a)-[e]->(b) " "WHERE e.interesting = true " "}" ) # Equivalent native chain from graphistry import n, e_forward g2 = g.gfql([n(), e_forward({"interesting": True}, hops=2) ]) g2.plot() **Same-Path Constraints (WHERE)** Example: Match an account and its owner when both steps share an attribute. .. code-block:: python from graphistry import n, e_forward, col, compare g_filtered = g.gfql( [ n({"type": "account"}, name="a"), e_forward(), n({"type": "user"}, name="c"), ], where=[compare(col("a", "owner_id"), "==", col("c", "owner_id"))], ) **Row-Pipeline `RETURN` Example** Example: Match people, filter rows, project columns, then sort/limit. .. code-block:: python from graphistry import n, e_forward, gt from graphistry.compute import rows, where_rows, return_, order_by, limit top_people = g.gfql([ n({"type": "Person"}), e_forward({"type": "FOLLOWS"}), n({"type": "Person", "score": gt(0)}, name="p"), rows(table="nodes", source="p"), where_rows(expr="score >= 50"), return_(["id", "name", "score"]), order_by([("score", "desc"), ("name", "asc")]), limit(10), ]) top_people._nodes **Local Cypher `CALL ... .write()` Example** Example: Enrich a graph locally, keep graph state, then run a later `MATCH`. .. code-block:: python g_enriched = g.gfql("CALL graphistry.degree.write()") assert not g_enriched._edges.empty top_degree = g_enriched.gfql( "MATCH (n) " "WHERE n.degree >= 2 " "RETURN n.id AS id, n.degree AS degree " "ORDER BY degree DESC, id ASC " "LIMIT 10" ) top_degree._nodes **Local Cypher row-returning `CALL` Example** Example: Omit `.write()` when you want procedure rows instead of an enriched graph. .. code-block:: python degree_rows = g.gfql("CALL graphistry.degree()") assert degree_rows._edges.empty degree_rows._nodes This row result uses ``nodeId`` as the row identifier, stores the projected procedure outputs in ``_nodes``, and clears ``_edges``. Use ``.write()`` when the next step needs graph topology. Example visualization (static): .. raw:: html
GFQL 2-hop example rendered with plot_static
2-hop "interesting" edges rendered with plot_static().
**Find Nodes 1-2 Hops Away and Label Each Hop** Example: Find nodes up to 2 hops away from node `"a"` and label each hop. .. code-block:: python from graphistry import n, e_undirected g_2_hops = g.gfql([ n({g._node: "a"}), e_undirected(name="hop1"), e_undirected(name="hop2") ]) first_hop_edges = g_2_hops._edges[ g_2_hops._edges["hop1"] == True ] print('Number of first-hop edges:', len(first_hop_edges)) **Filter by Date/Time** Example: Find recent transactions using temporal predicates. .. code-block:: python from graphistry import n, e_forward from graphistry.compute import gt, between from datetime import datetime, date, time import pandas as pd # Find transactions after a specific date recent = g.gfql([ n(), e_forward(edge_match={"timestamp": gt(pd.Timestamp("2023-01-01"))}), n() ]) # Find transactions in a date range during business hours business_hours_txns = g.gfql([ n(), e_forward(edge_match={ "date": between(date(2023, 6, 1), date(2023, 6, 30)), "time": between(time(9, 0), time(17, 0)) }), n() ]) **Query for Transaction Nodes Between Risky Nodes** Example: Find transaction nodes between two kinds of risky nodes. .. code-block:: python from graphistry import n, e_forward, e_reverse g_risky = g.gfql([ n({"risk1": True}), e_forward(to_fixed_point=True), n({"type": "transaction"}, name="hit"), e_reverse(to_fixed_point=True), n({"risk2": True}) ]) hits = g_risky._nodes[ g_risky._nodes["hit"] == True ] print('Number of transaction hits:', len(hits)) **Filter by Multiple Node Types Using `is_in`** Example: Filter nodes and edges by multiple types. .. code-block:: python from graphistry import n, e_forward, e_reverse, is_in g_filtered = g.gfql([ n({"type": is_in(["person", "company"])}), e_forward({"e_type": is_in(["owns", "reviews"])}, to_fixed_point=True), n({"type": is_in(["transaction", "account"])}, name="hit"), e_reverse(to_fixed_point=True), n({"risk2": True}) ]) hits = g_filtered._nodes[ g_filtered._nodes["hit"] == True ] print('Number of filtered hits:', len(hits)) **DAG Patterns with Let Bindings** GFQL's Let bindings enable you to compose complex graph analyses by defining named subgraphs and operations that can reference each other. Like variables in programming, Let bindings make it easy to manipulate multiple graphs and subgraphs within a single query, while maintaining all the benefits of GFQL like GPU acceleration. Traditional Python approach (manual variable management): .. code-block:: python # Traditional Python: Manually manage intermediate results persons = g.gfql([n({'type': 'person'})]) adults = persons.gfql([n({'age': ge(18)})]) friends = adults.gfql([e_forward({'type': 'knows'})]) # Each step requires careful tracking of which graph to operate on GFQL Let approach (declarative DAG with named bindings): .. doc-test: skip .. code-block:: python from graphistry import let, ref, n, e_forward, ge # GFQL Let: Define a DAG of named operations result = g.gfql(let({ 'persons': n({'type': 'person'}), 'adults': ref('persons', [n({'age': ge(18)})]), # Reference and filter persons 'connections': [ n({'type': 'person', 'age': ge(18)}), e_forward({'type': 'knows'}), n() # Find connections from adults ] })) # Access any named result from the DAG adults = result._nodes[result._nodes['adults']] connections = result._edges[result._edges['connections']] Key advantages of GFQL Let: - **Named subgraphs**: Create reusable named graph operations like constants in code - **Dependency management**: Automatically resolves dependencies between operations - **Composability**: Build complex multi-stage analyses from simpler named operations - **GPU preservation**: All operations maintain GPU acceleration when available - **Clean semantics**: Express complex graph analyses as clear, declarative DAGs Leveraging GPU Acceleration ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ GFQL is optimized to take advantage of GPU acceleration using `cudf` and RAPIDS. When you use GPU dataframes, GFQL automatically executes queries on the GPU for massive speedups. **Automatic GPU Acceleration** Example: Run GFQL queries with GPU dataframes. .. code-block:: python import cudf import graphistry # Load data into GPU dataframes e_gdf = cudf.read_parquet('edges.parquet') n_gdf = cudf.read_parquet('nodes.parquet') # Create a graph with GPU dataframes g_gpu = graphistry.edges(e_gdf, 'src', 'dst').nodes(n_gdf, 'id') # Run GFQL query (executes on GPU) g_result = g_gpu.gfql([ ... ]) # Your GFQL query here print('Number of resulting edges:', len(g_result._edges)) **Forcing GPU Mode** Example: Explicitly set the engine to ensure GPU execution. .. code-block:: python g_result = g_gpu.gfql([ ... ], engine='cudf') Run Remotely ~~~~~~~~~~~~~ You may want to run GFQL remotely such as if the data is remote, e.g., in Hub or cloud storage, and you have faster remote GPU servers for acting on it. **Bind to Remote Data and Query** Example: Bind to remote data and run queries on remote GPU resources. .. code-block:: python import graphistry from graphistry import n, e g = graphistry.bind(dataset_id='my-dataset-id') nodes_df = g.gfql_remote([ n() ])._nodes **Upload Data and Run GPU Python Remotely** Example: Upload local data to a remote GPU server and run full GPU Python tasks on it. .. code-block:: python import graphistry from graphistry import n, e # Fully self-contained so can be transferred def my_remote_trim_graph_task(g): # Trick: You can also put database fetch calls here! return (g .nodes(g._nodes[:10]) .edges(g._edges[:10]) ) # Upload any local graph data to the remote server g2 = g1.upload() print(g2._dataset_id, g2._nodes_file_id, g2._edges_file_id) # Compute on it locally g_result = g2.python_remote_g(my_remote_trim_graph_task) print('Number of resulting edges:', len(g_result._edges)) See also `python_remote_table()` and `python_remote_json()` for returning other types of data. Visualizing GFQL Results ~~~~~~~~~~~~~~~~~~~~~~~~~ GFQL integrates with PyGraphistry, allowing you to visualize your graphs with GPU-accelerated rendering. Example: Visualize high PageRank nodes. .. code-block:: python from graphistry import n, e # Compute PageRank using cuGraph (GPU) g_enriched = g_result.compute_cugraph('pagerank') # Filter nodes with high PageRank g_high_pagerank = g_enriched.gfql([ n(query='pagerank > 0.1'), e(), n(query='pagerank > 0.1') ]) # Plot the subgraph g_high_pagerank.plot() Example visualization (graphviz): .. graphviz:: digraph high_pagerank { rankdir=LR; node [shape=ellipse]; a [label="a\npagerank=0.18", style="filled,bold", fillcolor="#90EE90", penwidth=3, color="#228B22"]; b [label="b\npagerank=0.12", style="filled,bold", fillcolor="#90EE90", penwidth=3, color="#228B22"]; c [label="c\npagerank=0.05", shape=box, style=filled, fillcolor="#D3D3D3", color="#A9A9A9", fontcolor="#696969"]; tx1 [label="tx1", shape=diamond, style=filled, fillcolor="#D3D3D3", color="#A9A9A9", fontcolor="#696969"]; tx2 [label="tx2\npagerank=0.16", shape=diamond, style="filled,bold", fillcolor="#90EE90", penwidth=3, color="#228B22"]; a -> b [color="#228B22", penwidth=2]; b -> c [color="#A9A9A9"]; a -> tx1 [color="#A9A9A9"]; tx1 -> tx2 [color="#228B22", penwidth=2]; tx2 -> c [color="#A9A9A9"]; } Example visualization (interactive): .. raw:: html .. rubric:: Learn More Explore the following sections to dive deeper into GFQL's capabilities: - **10 Minutes to GFQL**: A quickstart guide to get you up and running. - :doc:`about` - **Hop & Chain Quick Reference**: Learn how to chain multiple operations to build complex queries. - :doc:`quick` - **Predicates Quick Reference**: Apply advanced filtering using predicates. - :doc:`predicates/quick` GFQL APIs ~~~~~~~~~~ Access detailed documentation of GFQL's API: - **Chain Operations**: Learn how to chain multiple operations to build complex queries. - :doc:`../api/gfql/chain` - **Hop Functions**: Understand how to traverse the graph using hop functions. - :doc:`../api/gfql/hop` - **Predicates**: Apply advanced filtering using predicates. - :doc:`../api/gfql/predicates`