/7.1/guides/Recent content in Guides on Kinetica DocsHugo -- gohugo.ioen/7.1/guides/quickstart-guide/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/quickstart-guide/Install Kinetica There are several different options for installing Kinetica on-premises or in the cloud (Azure or AWS). However, to get started within minutes (and for free) we recommend either of the following two routes: Kinetica Cloud Free: This is a free managed version with 10 GB of storage, which is hosted in the cloud. Follow the instructions for Kinetica Cloud Free to create an account and launch Kinetica./7.1/guides/solve_graph_seattle_backhaul/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/solve_graph_seattle_backhaul/The following is a complete example, using the Python API, of solving a graph created with Seattle road network data for a backhaul routing problem via the /solve/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. Prerequisites Python API Installation Data File Script Detail Constants Graph Creation Backhaul Routing Download & Run Prerequisites The prerequisites for running the backhaul routing solve graph example are listed below:/7.1/guides/cpp_guide/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/cpp_guide/The following guide provides step-by-step instructions to get started writing C++ applications using Kinetica. This guide demonstrates only a small set of the available API. A detailed description of the complete interface is available under C++ API Reference. API Download The source code for the C++ API is available for download from the GitHub repository kineticadb/kinetica-api-cpp. Follow the instructions in the included README file to build the API library./7.1/guides/fitting_data/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/fitting_data/The following guide provides step-by-step instructions to get started with fitting existing road network data to a graph. Since edges in graphs can only be composed of two nodes, the weight (or cost to travel) for complex WKT LINESTRINGs (e.g., more than two points) that typically define road networks must be assigned consistently to the segments composing the LINESTRING, as noted under Weights. There are two methods for calculating and assigning weights to a graph with edges derived from large WKT LINESTRINGs:/7.1/guides/isochrones/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/isochrones/The following is a complete example, using the Python API, of generating images containing isochrones via the /visualize/isochrone and /wms endpoints using an existing graph. Prerequisites Python API Installation PyPI Git Data File Script Detail Connections Constants Graph Creation Visualizing Isochrones Visualizing Isochrones Using WMS Download & Run Prerequisites The prerequisites for running the isochrones example are listed below: Graph server enabled Python API Isochrones example script DC Shape data CSV file Python API Installation The native Kinetica Python API is accessible through the following means:/7.1/guides/graph_rest_guide/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/graph_rest_guide/The following guide provides step-by-step instructions to get started with using the Network Graphs & Solvers in Kinetica. This guide demonstrates some key graph concepts as well as how to create and solve a graph using the Kinetica REST API. Prerequisites Data File Using the REST API Key Information and Concepts Data Graph Concepts Tutorial via REST Client Create Graph Solve the Graph (Shortest Path) Solve the Graph (Multiple Routing) Download & Run Prerequisites D./7.1/guides/graph_interface_guide/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/graph_interface_guide/The following guide provides step-by-step instructions to get started with using the Network Graphs & Solvers in Kinetica. This guide demonstrates some key graph concepts as well as how to create and solve a graph using the GAdmin graph GUI. Prerequisites Data File Key Information and Concepts Data Graph Concepts Tutorial via GUI Create Schema Create Graph Solve the Graph (Shortest Path) Solve the Graph (Multiple Routing) Prerequisites D.C. Shape data file Access to GAdmin Data File The tutorial makes use of the dc_shape dataset, which can be ingested from the {{< download "/examples/data/dc_shape./7.1/guides/graph_video_guide/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/graph_video_guide/In this video series, Hari Subhash walks through graphs in Kinetica, from high-level concepts to practical examples./7.1/guides/java_guide/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/java_guide/The following guide provides step-by-step instructions to get started writing Java applications using Kinetica. This guide demonstrates only a small set of the available API. A detailed description of the complete interface is available under Java API Reference. Prerequisites Data File API Download Maven Download Manual Download File Hierarchy Connecting to the Database Creating a Schema Creating a Type Creating a Table Inserting Data Key/Value Record In-Line Record CSV Retrieving Data Updating Records Deleting Records Alter Table Indexes Dictionary Encoding Filters Aggregates Joins Projections Union, Intersect, & Except Download & Run Prerequisites Data File The tutorial java file makes reference to a data file in the current directory./7.1/guides/js_guide/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/js_guide/The following guide provides step-by-step instructions to get started writing JavaScript applications using Kinetica. This guide demonstrates only a small set of the available API. A detailed description of the complete interface is available under JavaScript API Reference. API Download The source code for the JavaScript API is available for download from the GitHub repository kineticadb/kinetica-api-javascript. Follow the instructions in the included README file to use the API library./7.1/guides/jupyterlab/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/jupyterlab/Introduction Prerequisites Docker Install Setting Permissions Entering the License Key Pulling the Image Managing the Container Starting Using Stopping Exploring the Environment JupyterLab Contents Mounted Volumes Example Notebooks SVD Recommender and Visualization Example KJIO Utility Library Conclusion References Introduction JupyterLab is an integrated environment that can streamline the development of Python code and Machine Learning (ML) models in Kinetica. Jupyter notebooks integrate code execution, debugging, documentation, and visualization in a single document for consumption by multiple audiences./7.1/guides/match_graph_seattle_markov/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/match_graph_seattle_markov/The following is a complete example, using the Python API, of matching GPS sample data to road network data via the /match/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. Prerequisites Python API Installation Data Files Script Detail Constants Graph Creation Matching the Graph without Fold-over Filtering Matching the Graph with Fold-over Filtering Download & Run Prerequisites The prerequisites for running the match graph example are listed below:/7.1/guides/solve_graph_nyctaxi_multi_route/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/solve_graph_nyctaxi_multi_route/The following is a complete example, using the Python API, of solving a graph created with NYC Taxi data for a multiple routing problem via the /solve/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. Prerequisites Python API Installation Data File Script Detail Constants Graph Creation Multiple Routing Download & Run Prerequisites The prerequisites for running the multiple routing solve graph example are listed below:/7.1/guides/solve_graph_seattle_multi_route/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/solve_graph_seattle_multi_route/The following is a complete example, using the Python API, of solving a graph created with Seattle road network data for a multiple routing problem via the /solve/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. Prerequisites Python API Installation Data File Script Detail Constants Graph Creation Multiple Routing Download & Run Prerequisites The prerequisites for running the multiple routing solve graph example are listed below:/7.1/guides/match_graph_dc_multi_supply_demand/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/match_graph_dc_multi_supply_demand/The following is a complete example, using the Python API, of matching stores (demands) to depots/trucks (suppliers) via the /match/graph endpoint using an underlying graph created from a modified OpenStreetMap (OSM) dataset. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. Prerequisites Python API Installation Data File Script Detail Constants Table Setup Graph Creation Multiple Supply Demand with Priority Solution Analysis Multiple Supply Demand with Priority and Max Trip Cost Download & Run Prerequisites The prerequisites for running the match graph example are listed below:/7.1/guides/nodejs_guide/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/nodejs_guide/The following guide provides step-by-step instructions to get started writing Node.js applications using the client-side Node.js module for Kinetica. This guide demonstrates only a small set of the available API. A detailed description of the complete interface is available under Node.js API Reference. API Download The source code for the Node.js API is available for download from the GitHub repository kineticadb/kinetica-api-javascript. Follow the instructions in the included README file to use the API library./7.1/guides/solve_graph_nyctaxi_page_rank/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/solve_graph_nyctaxi_page_rank/The following is a complete example, using the Python API, of solving a graph created with NYC Taxi data for a page rank problem via the /solve/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. Prerequisites Python API Installation Data File Script Detail Constants Graph Creation Page Rank Download & Run Prerequisites The prerequisites for running the page rank solve graph example are listed below:/7.1/guides/python_guide/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/python_guide/The following guide provides step-by-step instructions to get started writing Python applications using Kinetica. This guide demonstrates only a small set of the available API. A detailed description of the complete interface is available under Python API Reference. Prerequisites Python API Installation PyPI Git Data File Connecting to the Database Creating a Schema Creating a Type Creating a Table Inserting Data Key/Value Record In-Line Record CSV Retrieving Data Updating Records Deleting Records Alter Table Indexes Dictionary Encoding Filters Aggregates Joins Projections Union, Intersect, & Except Download & Run Prerequisites The prerequisites for running the match graph example are listed below:/7.1/guides/udf_python_guide/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/udf_python_guide/The following guide provides step-by-step instructions to get started writing and running UDFs in Python. This example is a simple distributed UDF that copies data from one table to another using a CSV configuration file to determine on which processing node(s) data will be copied. Standard (non-replicated) tables have their data distributed across all processing nodes, while replicated tables have all of their data on every processing node. In this example, we'll use a standard table and copy only the portions of its data that reside on the nodes named in the CSV file./7.1/guides/query_graph_social/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/query_graph_social/The following is a complete example, using the Python API, of querying social relationship data via the /query/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. Prerequisites Python API Installation Script Detail Constants Table Setup Graph Creation Querying the Graph Download & Run Prerequisites The prerequisites for running the query graph example are listed below: Graph server enabled Python API Query graph script Python API Installation The native Kinetica Python API is accessible through the following means:/7.1/guides/solve_graph_seattle_shortest_path/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/solve_graph_seattle_shortest_path/The following is a complete example, using the Python API, of solving a graph created with Seattle road network data for a shortest path problem via the /solve/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. Prerequisites Python API Installation PyPI Git Data File Script Detail Constants Graph Creation Shortest Path Single Source to Single Destination Single Source to Many Destinations Many Sources to Many Destinations Download & Run Prerequisites The prerequisites for running the shortest path solve graph example are listed below:/7.1/guides/solve_graph_dc_shortest_path_turn/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/solve_graph_dc_shortest_path_turn/The following is a complete example, using the Python API, of solving a graph created with a Washington, D.C. HERE dataset for a shortest path problem with turn penalties via the /solve/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. For more information on turn penalties and restrictions, see Using Turn-based Weights & Restrictions. Prerequisites Python API Installation Data File Script Detail Constants Table Setup Graph Creation Shortest Path Download & Run Prerequisites The prerequisites for running this solve graph example are listed below:/7.1/guides/sql_guide/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/sql_guide/The following guide provides step-by-step instructions to get started interacting with Kinetica using SQL. This guide demonstrates some of the most common and useful SQL commands, but a detailed look into the full SQL support is available on the SQL page. Prerequisites Data File Creating a Type and Table Inserting Data Retrieving Data Updating Records Deleting Records Alter Table Indexes Dictionary Encoding Filters & Aggregates Subqueries Common Table Expression / With Joins Create Table As Union, Intersect, and Except Truncate Complete Sample Prerequisites For a description of how to configure various clients for issuing SQL commands, see Connecting./7.1/guides/udf-sql-sentence-transformers/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/udf-sql-sentence-transformers/Download & Run UDF Detail SQL Management Script Create Tables Load UDF & Data Files Create a Python UDF Environment Register UDF & UDTF Execute UDF & UDTF Python UDF Script Initialize UDF Perform Semantic Search Write Output Data The following is a SQL-managed UDF written in the Python UDF API that performs a semantic search for a given sentence on a baseline set of sentences, ranking the baseline sentences on their similarity to the given one./7.1/guides/table_monitor/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/table_monitor/The following is an excerpted tutorial, extracted from a complete example using the Python Table Monitor API, demonstrating the basic use of the three table monitor types: table insert monitor - watches a table for newly inserted records and can make use of those records elsewhere table update monitor - watches a table for updates and reports the number of records updated in each update request issued table delete monitor - watches a table for deletes and reports the number of records deleted in each delete request issued The table monitors will be managed via instances of the GPUdbTableMonitor./7.1/guides/wkt_data_geo_functions/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/wkt_data_geo_functions/The following sections demonstrate how to ingest and work with WKT data as well as how to use geospatial functions via SQL and the Kinetica Python API. Details about the geospatial functions can be found under Geospatial/Geometry Functions. All geospatial functions are compatible in both native API and SQL. Prerequisites Loading Sample Data Examples via SQL Creating a Table and Inserting WKT Data Scalar Functions Aggregation Functions Joins Equality Examples via the Python API Creating a Table and Inserting WKT Data Scalar Functions Aggregation Functions Joins Equality Complete Samples Prerequisites KiSQL (if running the SQL examples) Python API (if running the Python examples) Sample CSV data files, which should be downloaded & copied to the host running the SQL client.