All Classes and Interfaces
Class
Description
Argument object used in auto-serialization examples.
This example demonstrates how to create, read and write to Caches with GridGain 9, JDBC and external database H2.
This example demonstrates the usage of the
IgniteCompute.execute(org.apache.ignite.compute.JobTarget, org.apache.ignite.compute.JobDescriptor<T, R>, T) API.This example demonstrates the usage of the
IgniteCompute API with Go WASM jobs.This code demonstrates the usage of the
JobExecution interface that allows to get job statuses and, for example, handle
failures.Job that prints provided word.
This example demonstrates a partition-aware Map/Reduce pattern using the
IgniteCompute.executeMapReduce(org.apache.ignite.compute.TaskDescriptor<T, R>, T) API
together with the __PARTITION_ID virtual SQL column.Job that counts persons in a single partition, identified by partition ID passed as the job argument.
MapReduce task that counts persons across all partitions of the
Person table.This example demonstrates the usage of the
IgniteCompute API with Rust WASM jobs.This example demonstrates the usage of the
IgniteCompute API with WASM jobs.This example demonstrates the usage of the basic client API without any fine-tuning.
Compute job for Batch Custom ML predictions.
Compute job for Custom ML predictions.
Defines the input structure for custom ML model inference.
CustomInputMarshaller<I extends org.gridgain.ml.model.MlJobParameters>
A standard Marshaller for all input types for ML.
Represents the output of a custom ML model inference.
Handles serialization and deserialization of lists of custom ML outputs.
A standard Marshaller for all list based output types for ML.
Example demonstrating custom POJO serialization using custom marshallers
on both client and server side.
Custom translator for processing model input and output for custom ML inference.
Factory for creating instances of
CustomTranslator.Utility class for deploying Ignite compute units.
This example demonstrates how to use the streaming API to catch both asynchronous errors during background streaming and immediate submission errors.
This example demonstrates how to use the streaming API to simulate a fraud detection process, which typically involves intensive
processing of each transaction using ML models.
Receiver that processes transactions and detects potential fraud.
This example demonstrates the usage of the basic client API without any fine-tuning.
This example demonstrates the usage of the basic client API with additional configuration parameters.
This example demonstrates the usage of the
KeyValueView API.This example demonstrates the usage of the
KeyValueView API with user-defined POJOs.This example demonstrates the usage of the
Mapper API with a custom TypeConverter.Base utility class for running ML inference examples in GridGain.
This example demonstrates:
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This example demonstrates:
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This example demonstrates:
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Demonstrates running a TensorFlow-based Universal Sentence Encoder model on GridGain.
Base class for ML examples with functions to
1.
This example demonstrates how to use the streaming API to implement a receiver that processes data containing customer and address
information, and updates two separate tables on the server.
This example demonstrates the usage of Near Cache with an embedded Ignite server node.
This example demonstrates the usage of Near Cache with a client connecting to an existing Ignite cluster.
This example demonstrates a usage of the PageMemory storage engine configured with a persistent data region.
Represents a Person entity with database mapping.
Compute job for PyTorch QA ML predictions.
Serializable wrapper for QAInput
PytorchQAInputMarshaller<I extends org.gridgain.ml.model.MlJobParameters>
Handles serialization and deserialization for PyTorch QA jobs.
Serializes and deserializes PyTorch QA model outputs.
Translator for PyTorch-based Question Answering models.
Factory for creating instances of
PytorchQATranslator.This example demonstrates the usage of the
RecordView API.This example demonstrates the usage of the
RecordView API with user-defined POJOs.This example demonstrates a usage of the RocksDB storage engine.
This example demonstrates the usage of the
IgniteCompute.executeAsync(org.apache.ignite.compute.JobTarget, org.apache.ignite.compute.JobDescriptor<T, R>, T) API with various serialization approaches.This example demonstrates how to use the streaming API to configure the data streamer, insert account records into the existing Accounts
table and then delete them.
Examples of using SQL API.
This example demonstrates the usage of the Apache Ignite JDBC driver.
This example demonstrates the usage of the { @link KeyValueView} API.
TensorFlowInputMarshaller<I extends org.gridgain.ml.model.MlJobParameters>
Handles serialization and deserialization of
MlJobParameters for TensorFlow inference jobs.Serializes and deserializes TensorFlow model outputs.
Compute job for TensorFlow Sentence Encoder ML predictions.
This example demonstrates the usage of the Ignite Transactions API.
Custom receiver class that extracts data from the provided source and write it into two separate tables: Customers and Addresses
This example demonstrates a usage of the PageMemory storage engine configured with an in-memory data region.