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Kinetica is blazing fast on a GPU but even on CPUs we continue to outperform leading analytical databases in TPC-DS benchmarks. Comparisons are made to Kinetica from a geomean of the queries that a database was able to run. So Clickhouse was 13x slower overall on the 8 queries it was able to successfully complete. An independently designed and performed Spatial and Time Series Benchmark to help organizations evaluate database technologies suitable for IoT and sensor data workloads. This report evaluates the performance and functionality of leading cloud databases with built in geospatial, temporal, and graph capabilities Tests were run with 200GB (SF200) of sample data comprising 24 tables in a snowflake schema. The tables store web, catalog and store sales from an imaginary retailer. The largest fact table had well over a billion rows. Benchmarking was done using a consistent distributed hardware configuration of four Azure virtual machines: E48s v4 (48 vCPU, 384 GB RAM) with 2TB premium SSD, or equivalent setup. Kinetica’s unique native vectorized join engine is able to process chunks of data in parallel, rather than sequentially working through rows of data line by line. This delivers very quick results, particularly with complex ad-hoc analysis of complex data. Kinetica has been in development for over a decade. It is mature and battle-tested and able to reliably parse and deliver results on complex SQL queries. Kinetica is typically able to run all 99 queries of the TPC-DC harness. Kinetica fast, real-time capabilities extend out to it’s versatile

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The following is a complete example, using the Python UDF API, of a non-CUDAUDF that demonstrates how to create pandas dataframes and insert them intotables in Kinetica. This example (andothers) can be found in the Python UDF APIrepository; this repository can be downloaded/cloned fromGitHub.ReferencesPython UDF Reference -- detaileddescription of the entire UDF APIRunning UDFs -- detailed description onrunning Python UDFsExample UDFs -- example UDFs written inPythonPrerequisitesThe general prerequisites for using UDFs in Kinetica can be found underUDF Prerequisites.ImportantThis example cannot run on Mac OSXThe following items are also necessary:Python 3MinicondaNoteVisit the Conda website to downloadthe Miniconda installer for Python 3.UDF API DownloadThis example requires local access to the Python UDF API repository. In thedesired directory, run the following but be sure to replace with the name of the installed Kinetica version, e.g,v7.2:git clone -b release/ --single-branch ScriptsThere are four files associated with the pandas UDF example, allof which can be found in the Python UDF API repo.A database setup script (test_environment.py) that is called from theinitialization scriptAn initialization script (setup_db.py) that creates the output tableA script (register_execute_UDF.py) to register and execute the UDFA UDF (df_to_output_UDF.py) that creates a pandas dataframe and insertsit into the output tableThis example runs inside a Conda environment. The environment can beautomatically configured using the conda_env_py3.yml file found in thePython UDF API repository.In the same directory you cloned the API, change directory into the rootfolder of the Python UDF API repository:cd kinetica-udf-api-python/Create the Conda environment, replacing with thedesired name:conda env create --name --file conda_env_py3.ymlNoteIt may take a few minutes to create the environment.Verify the environment was created properly:conda info --envsActivate the new environment:conda activate Install PyGDF:conda install -c numba -c conda-forge -c gpuopenanalytics/label/dev -c defaults pygdf=0.1.0a2Install the Kinetica Python API:pip install gpudb~=7.2.0Add the Python UDF API repo's root directory to the PYTHONPATH:export PYTHONPATH=$(pwd):$PYTHONPATHEdit the util/test_environment.py script for the correct database url,user, and password for your Kinetica instance:URL = ' = 'admin'PASSWORD = 'admin123'UDF DeploymentChange directory into the UDF Pandas directory:cd examples/UDF_pandasRun the UDF initialization script:python setup_db.pyRun the execute script for the training UDF:python register_execute_UDF.py --url --username --password Verify the results in GAdmin, either on thelogging page or in the unittest_df_outputoutput table on the table view page.Execution DetailThis example details using a distributed UDF to create and ingest a pandasdataframe into Kinetica. The df_to_output_UDF proc creates the dataframeand inserts it into the output table, unittest_df_output.The dataframe has a shape of (3, 3) and will get inserted into theoutput table n number of times, where n is equal to the number ofprocessing nodes available in each processing node container registered inKinetica.The output table contains 3 columns:id -- an integer columnvalue_long -- a long columnvalue_float -- a float columnDatabase SetupThe setup script, setup_db.py, which creates the output table for the UDF,imports the test_environment.py script to access its methods:1from util import test_environment as teThe script calls two methods from test_environment.py: one to create theschema used to contain the example tables and one to create the output tableswhere the values for the output table's name and type are passed in:12te.create_schema()te.create_test_output_table(te.TEST_OUTPUT_TABLE_NAME, te.TEST_DATA_TYPE_1)The

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Optionstable = gpudb.GPUdbTable( columns, name = "example.customer_order_interval_partition_by_hour_interval", db = kinetica, options = { "partition_type": "INTERVAL", "partition_keys": "purchase_ts", "partition_definitions": "STARTING ('2014-01-01') INTERVAL (INTERVAL '1' HOUR)" })ListList partitioning improves the performance of filters on the list partitionvalues.List partitions are defined in one of two schemes:ManualAutomaticThe following data types are supportedpartition key types; other types can be used inside a column expression, aslong as the type of the final expression is one of these:numeric types:base type: int, long, float, doubleeffective type: decimal, ulongdate/time types: date, time, datetime, timestampstring types: string, charN, uuidA table is list-partitioned at creation time, by calling the/create/table endpoint with a partition_type option ofLIST and partition_keys & partition_definitions options setappropriately. Set is_automatic_partition option to true in order touse an automatic partitioning scheme.Manual PartitioningManual partitions are defined as a specific list of values, and all recordshaving partition key values that are in one of those lists are assigned tothe corresponding partition. A given list entry cannot exist in more thanone list, meaning one record cannot qualify for more than one partition.All records which don’t belong to any of the named partitions will be placedin the default partition. If a new partition is added, any records in thedefault partition that would qualify for being placed in the new partitionwill be moved there.Single Column Partition KeyFor example, to create a manual list-partitioned table with the followingcriteria, in Python:partitioned on the date/time of the orderpartitions for years:2014 - 2016201720182019records not in that list go to the default partition12from gpudb import GPUdbRecordColumn as GRCfrom gpudb import GPUdbColumnProperty as GCP 1 2 3 4 5 6 7 8 9101112131415161718192021222324# Create a column listcolumns = [ [ "id", GRC._ColumnType.INT ], [ "customer_id", GRC._ColumnType.INT, GCP.SHARD_KEY ], [ "total_price", GRC._ColumnType.STRING, GCP.DECIMAL ], [ "purchase_ts", GRC._ColumnType.LONG, GCP.TIMESTAMP ]]# Create a manual list-partitioned table using the column list and the# partition optionstable = gpudb.GPUdbTable( columns, name = "example.customer_order_manual_list_partition_by_year", db = kinetica, options = { "partition_type": "LIST", "partition_keys": "YEAR(purchase_ts)", "partition_definitions": "" \ "order_2014_2016 VALUES (2014, 2015, 2016)," \ "order_2017 VALUES (2017)," \ "order_2018 VALUES (2018)," \ "order_2019 VALUES (2019)" })To add a partition to a manual list-partitioned table, in Python:1234table.alter_table( action =. Listen to Kinetica latest songs and albums online, download Kinetica songs MP3 for free, watch Kinetica hottest music videos and interviews and learn about Kinetica biography on Boomplay. Download Onyx (Mixed) - Kinetica MP3 song on Boomplay and listen Onyx (Mixed) - Kinetica offline with lyrics. Onyx (Mixed) - Kinetica MP3 song from the Kinetica’s album FSOE 750 -

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OverviewThe concept of tables is at the heart of Kinetica interactions. A tableis a data container associated with a specific type(set of columns & properties), much like tables in other database platforms.When querying a table, the result set will also have a single typeassociated with it.Each table must exist within a schema.Name ResolutionA table can be addressed by qualified name, prefixing the table name withthe name of its containing schema, separated by a dot; e.g.:.Tables referenced without a schema will be looked for in the user'sdefault schema, if assigned; effectively, a referencelike this:is resolved like this:.Naming CriteriaEach table is identified by a name, which must meet the following criteria:Between 1 and 200 characters longFirst character is alphanumeric or an underscoreAlphanumeric, including spaces and these symbols:_ # { } [ ] ( ) : -Unique within its containing schema--cannot have the same name as anothertable or view in the same schema, but can have thesame name as any schemaColumn names must meet the following criteria:Between 1 and 200 characters longFirst character is alphanumeric or an underscoreAlphanumeric, including these symbols:_ { } [ ] : .ExampleUsing the /create/table endpoint, you can create an emptytable that can later hold records.For example, in Python, to create a simple 2-column table:12from gpudb import GPUdbRecordColumn as GRCfrom gpudb import GPUdbColumnProperty as GCP 1 2 3 4 5 6 7 8 9101112# Create a column listcolumns = [ [ "id", GRC._ColumnType.INT ], [ "name", GRC._ColumnType.STRING, GCP.CHAR64 ]]# Create a simple table using the column listgpudb.GPUdbTable( columns, name = "example.simple_table", db = kinetica)DistributionTable data can be distributed across the Kinetica clusterusing one of two schemes: sharding & replication. Within the shardingscheme, data can be distributed either by key orrandomly in the absence of ashard key.ShardingSharding is the distribution of table data by hashing aparticular value for each record, and by that hash, determining on whichKinetica cluster node the record will reside.The benefit of sharding is that it allows distributed queries to be runagainst a given data set, with each node responsible for managing its portion ofthe overall data set, while only storing each record once across the cluster.The

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Methods in test_environment.py require a connection to Kinetica.This is done by instantiating an object of the GPUdb class with aprovided connection URL. See Connecting via API for details on the URLformat and how to look it up.1234URL = ' = 'admin'PASSWORD = 'admin123'DB_HANDLE = gpudb.GPUdb(host=[URL], username=USER, password=PASSWORD)The create_schema() method creates the schema that will contain the tableused in the example:1SCHEMA = 'example_udf_python'1DB_HANDLE.create_schema(SCHEMA, options={'no_error_if_exists': 'true'})The create_test_output_table() method creates the type and table for theoutput table, but the table is removed first if it already exists:123456TEST_DATA_TABLE_NAME_1 = SCHEMA + '.unittest_toy_data'TEST_DATA_TYPE_1 = [ ['id', gpudb.GPUdbRecordColumn._ColumnType.INT], ['value_long', gpudb.GPUdbRecordColumn._ColumnType.LONG], ['value_float', gpudb.GPUdbRecordColumn._ColumnType.FLOAT]]123if DB_HANDLE.has_table(table_name=table_name)['table_exists']: DB_HANDLE.clear_table(table_name)test_data_table = gpudb.GPUdbTable(table_type, table_name, db=DB_HANDLE)UDF (df_to_output_UDF.py)First, packages are imported to access the Kinetica Python UDF API andpandas:12from kinetica_proc import ProcDataimport pandas as pdNext, the file gets a handle to the ProcData() class:To output the number of values found on each processing node and processing nodecontainer, the rank and tom number in the request info map pointing to thecurrent instance of the UDF are mapped and displayed:123rank_number = proc_data.request_info['rank_number']tom_number = proc_data.request_info['tom_number']print('\nUDF pandas proc output test r{}_t{}: instantiated.'.format(rank_number, tom_number))The dataframe is created:12data = {'id': pd.Series([1, 12, 123]), 'value_long': pd.Series([2, 23, 24]), 'value_float': pd.Series([0.2, 2.3, 2.34])}df = pd.DataFrame(data)Get a handle to the output table from the proc(unittest_df_output). Its size is expanded to match the shape of thedataframe; this will allocated enough memory to copy all records in thedataframe to the output table. Then the dataframe is assigned to the outputtable:123output_table = proc_data.output_data[0]output_table.size = df.shape[0]proc_data.from_df(df, output_table)UDF Registration (register_execute_UDF.py)To interact with Kinetica, an object of the GPUdb class is instantiatedwhile providing the connection URL of the database server.1main(gpudb.GPUdb(host=[args.url], username=args.username, password=args.password))To upload the df_to_output_UDF.py and kinetica_proc.py files toKinetica, they will first need to be read in as bytes and added to a file datamap:123456file_paths = ["df_to_output_UDF.py", "../../kinetica_proc.py"]files = {}for script_path in file_paths: script_name = os.path.basename(script_path) with open(script_path, 'rb') as f: files[script_name] = f.read()After the files are placed in a data map, the distributed Pandas_df_outputproc is created in Kinetica and the files are associated with it:1234proc_name = 'Pandas_df_output'print("Registering proc...")response = db_handle.create_proc(proc_name, 'distributed', files, 'python', [file_paths[0]], {})print(response)NoteThe proc requires the proper command and args to be executed. Inthis case, the assembled command line would be:python df_to_output_UDF.pyFinally, after the proc is created, it is executed. The output table createdin the Database Setup section is passed in here:123print("Executing proc...")response = db_handle.execute_proc(proc_name, {}, {}, [], {}, [te.TEST_OUTPUT_TABLE_NAME], {})print(response)

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3 4 5 6 7 8 91011121314151617181920# Create a column listcolumns = [ [ "id", GRC._ColumnType.INT ], [ "customer_id", GRC._ColumnType.INT, GCP.SHARD_KEY ], [ "total_price", GRC._ColumnType.STRING, GCP.DECIMAL ], [ "purchase_ts", GRC._ColumnType.LONG, GCP.TIMESTAMP ]]# Create an interval-partitioned table using the column list and the# partition optionstable = gpudb.GPUdbTable( columns, name = "example.customer_order_interval_partition_by_year", db = kinetica, options = { "partition_type": "INTERVAL", "partition_keys": "YEAR(purchase_ts)", "partition_definitions": "STARTING (2014) INTERVAL (1)" })To create an interval-partitioned table with the following criteria:partitioned on the date/time of the orderone partition for each day from January 1st, 2014 onlater day partitions are added as necessaryrecords prior to 2014 go to the default partition12from gpudb import GPUdbRecordColumn as GRCfrom gpudb import GPUdbColumnProperty as GCP 1 2 3 4 5 6 7 8 91011121314151617181920# Create a column listcolumns = [ [ "id", GRC._ColumnType.INT ], [ "customer_id", GRC._ColumnType.INT, GCP.SHARD_KEY ], [ "total_price", GRC._ColumnType.STRING, GCP.DECIMAL ], [ "purchase_ts", GRC._ColumnType.LONG, GCP.TIMESTAMP ]]# Create an interval-partitioned table using the column list and the# partition optionstable = gpudb.GPUdbTable( columns, name = "example.customer_order_interval_partition_by_day_timestampdiff", db = kinetica, options = { "partition_type": "INTERVAL", "partition_keys": "TIMESTAMPDIFF(DAY, '2014-01-01', purchase_ts)", "partition_definitions": "STARTING (0) INTERVAL (1)" })The same interval-partitioned scheme above can be created using the timestampcolumn directly, and date-aware INTERVAL syntax(supported date/time units are the same as those listed with theTIMESTAMPADD function under Date/Time Base Functions ):12from gpudb import GPUdbRecordColumn as GRCfrom gpudb import GPUdbColumnProperty as GCP 1 2 3 4 5 6 7 8 91011121314151617181920# Create a column listcolumns = [ [ "id", GRC._ColumnType.INT ], [ "customer_id", GRC._ColumnType.INT, GCP.SHARD_KEY ], [ "total_price", GRC._ColumnType.STRING, GCP.DECIMAL ], [ "purchase_ts", GRC._ColumnType.LONG, GCP.TIMESTAMP ]]# Create an interval-partitioned table using the column list and the# partition optionstable = gpudb.GPUdbTable( columns, name = "example.customer_order_interval_partition_by_day_interval", db = kinetica, options = { "partition_type": "INTERVAL", "partition_keys": "purchase_ts", "partition_definitions": "STARTING ('2014-01-01') INTERVAL (INTERVAL '1' DAY)" })This scheme can be easily modified to create an hourly partition instead: 1 2 3 4 5 6 7 8 91011121314151617181920# Create a column listcolumns = [ [ "id", GRC._ColumnType.INT ], [ "customer_id", GRC._ColumnType.INT, GCP.SHARD_KEY ], [ "total_price", GRC._ColumnType.STRING, GCP.DECIMAL ], [ "purchase_ts", GRC._ColumnType.LONG, GCP.TIMESTAMP ]]# Create an interval-partitioned table using the column list and the# partition. Listen to Kinetica latest songs and albums online, download Kinetica songs MP3 for free, watch Kinetica hottest music videos and interviews and learn about Kinetica biography on Boomplay. Download Onyx (Mixed) - Kinetica MP3 song on Boomplay and listen Onyx (Mixed) - Kinetica offline with lyrics. Onyx (Mixed) - Kinetica MP3 song from the Kinetica’s album FSOE 750 -

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Table using the column listgpudb.GPUdbTable( columns, name = "example.sharded_table", db = kinetica)To create a table with a composite shard key on id_a & id_b:12from gpudb import GPUdbRecordColumn as GRCfrom gpudb import GPUdbColumnProperty as GCP 1 2 3 4 5 6 7 8 91011121314# Create a column list, annotating the intended shard key columns with# shard_keycolumns = [ [ "id_a", GRC._ColumnType.INT, GCP.SHARD_KEY ], [ "id_b", GRC._ColumnType.INT, GCP.SHARD_KEY ], [ "name", GRC._ColumnType.STRING, GCP.CHAR64 ]]# Create a table with a composite shard key using the column listgpudb.GPUdbTable( columns, name = "example.composite_shard_key_table", db = kinetica)Lastly, to create a table with a shard key that is not the primary key (butconsists of a proper subset of the columns), with the primary key on id_a& id_b and the shard key on id_b:12from gpudb import GPUdbRecordColumn as GRCfrom gpudb import GPUdbColumnProperty as GCP 1 2 3 4 5 6 7 8 9101112131415# Create a column list, annotating the intended primary key columns with# primary_key and the intended shard key column with shard_key in additioncolumns = [ [ "id_a", GRC._ColumnType.INT, GCP.PRIMARY_KEY ], [ "id_b", GRC._ColumnType.INT, GCP.PRIMARY_KEY, GCP.SHARD_KEY ], [ "name", GRC._ColumnType.STRING, GCP.CHAR64 ]]# Create a table with a composite primary key and a shard key that consists# of a proper subset of the primary key columns, using the column listgpudb.GPUdbTable( columns, name = "example.shard_key_not_primary_key_table", db = kinetica)Note also that sharding applies only to non-replicated tables, and thedefault /create/table distribution scheme implied in theexample above is the non-replicated one. If an attempt is made to create atable as replicated with a column set that specifies a shard key, therequest will fail. An attempt to create a replicated table with a shard keythat is the same as the primary key (all columns marked with primary_keyare also marked with shard_key) will succeed in creating areplicated table--the shard key designation will be ignored.Foreign KeysForeign key is a designation that can be applied to one or more columns in asource table that relate them to a matching set ofprimary key columns in a target table. A foreign keycomposed of multiple columns is known as a composite foreign key.NoteA foreign key can only target

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Chunk_size, which is set in the system configuration.See the Persistence section of theConfiguration Reference for details.While this scheme was designed for colocating tracks fromtrack tables into the same partition, itcan be used with any table type, for suitable use cases.A table is series-partitioned at creation time, by calling the/create/table endpoint with a partition_type option ofSERIES, partition_definitions set to the percentage full the openpartition should be before its partition key set is closed and a new onecreated, and partition_keys set to whichever columns should be included inthe partition key.NoteThe default partition fill threshold is 50%.For example, to create a series-partitioned table with the followingcriteria, in Python:partitioned on the customer of each orderpartitions with closed key sets will contain all orders from a set of uniquecustomers50% fill threshold12from gpudb import GPUdbRecordColumn as GRCfrom gpudb import GPUdbColumnProperty as GCP 1 2 3 4 5 6 7 8 91011121314151617181920# Create a column listcolumns = [ [ "id", GRC._ColumnType.INT ], [ "customer_id", GRC._ColumnType.INT, GCP.SHARD_KEY ], [ "total_price", GRC._ColumnType.STRING, GCP.DECIMAL ], [ "purchase_ts", GRC._ColumnType.LONG, GCP.TIMESTAMP ]]# Create a series-partitioned table using the column list and the# partition optionstable = gpudb.GPUdbTable( columns, name = "example.customer_order_series_partition_by_customer", db = kinetica, options = { "partition_type": "SERIES", "partition_keys": "customer_id", "partition_definitions": "PERCENT_FULL 50" })To create a series-partitioned track table with the following criteria, inPython:partitioned on the track IDpartitions with closed key sets will contain all points from a unique set oftracks25% fill threshold12from gpudb import GPUdbRecordColumn as GRCfrom gpudb import GPUdbColumnProperty as GCP 1 2 3 4 5 6 7 8 9101112131415161718192021# Create a column listcolumns = [ [ "id", GRC._ColumnType.INT ], [ "TRACKID", GRC._ColumnType.STRING ], [ "x", GRC._ColumnType.DOUBLE ], [ "y", GRC._ColumnType.DOUBLE ], [ "TIMESTAMP", GRC._ColumnType.LONG, GCP.TIMESTAMP ]]# Create a series-partitioned track table using the column list and the# partition optionstable = gpudb.GPUdbTable( columns, name = "example.route_series_partition_by_track", db = kinetica, options = { "partition_type": "SERIES", "partition_keys": "TRACKID", "partition_definitions": "PERCENT_FULL 25" })Primary KeysPrimary key is a designation that can be applied to one or more columns in atable. A primary key composed of multiple columns is known asa composite primary key.PurposeThe primary key is used to ensure the uniqueness of the data. Listen to Kinetica latest songs and albums online, download Kinetica songs MP3 for free, watch Kinetica hottest music videos and interviews and learn about Kinetica biography on Boomplay.

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Import GPUdbColumnProperty as GCP 1 2 3 4 5 6 7 8 910111213# Create a column list, annotating the intended primary key column with# primary_keycolumns = [ [ "id", GRC._ColumnType.INT, GCP.PRIMARY_KEY ], [ "name", GRC._ColumnType.STRING, GCP.CHAR64 ]]# Create a table with a primary key using the column listgpudb.GPUdbTable( columns, name = "example.primary_key_table", db = kinetica)To create a table with a composite primary key on id_a & id_b:12from gpudb import GPUdbRecordColumn as GRCfrom gpudb import GPUdbColumnProperty as GCP 1 2 3 4 5 6 7 8 91011121314# Create a column list, annotating the intended primary key columns with# primary_keycolumns = [ [ "id_a", GRC._ColumnType.INT, GCP.PRIMARY_KEY ], [ "id_b", GRC._ColumnType.INT, GCP.PRIMARY_KEY ], [ "name", GRC._ColumnType.STRING, GCP.CHAR64 ]]# Create a table with a composite primary key using the column listgpudb.GPUdbTable( columns, name = "example.composite_primary_key_table", db = kinetica)Soft Primary KeysSoft primary key is a designation that can be applied to one or more columnsin a table. A soft primary key composed of multiplecolumns is known as a composite soft primary key.PurposeThe soft primary key is a designation given to a column or set of columns forwhich the user takes on the responsibility for maintaining uniqueness. Thisexternally-managed uniqueness changes system processing in the following ways:Some join operations can still be optimized without aprimary key index if the values are knownto be unique.Memory consumption is reduced, as the soft primary key does not require aprimary key index be resident in theRAM tier, nor does it allow thepossibility of using a relational index,also saving memory.Ingest performance is improved, as the server doesn't have the burden ofchecking uniqueness of ingested data or updating the primary key indexwith newly ingested key values.The benefit of using a soft primary key is largely dependent on use case, butworkloads that involve heavy ingest or that consistently reach and exceed memoryconstraints may see improved performance.DesignationBy default, a table has no soft primary key. One must be explicitlydesignated in the creation of the type schemaassociated with the table.Only one soft primary key can exist per table.NoteThe following data types cannot be usedas all or part of a soft primary key:WKTJSONvectorarray of any typeRelation to