43 docs tagged with "Technology"

Association Roles define the relationship types between entities. Each role specifies the multiplicity and naming for both directions of the relationship (inbound and outbound).

Associations define relationships between entities in the runtime model. This chapter describes how to query and navigate associations.

Attributes are reusable property definitions that can be applied to types. Each attribute has a value type and optional constraints.

AutoIncrements provide automatic value generation for attributes when entities are inserted into the repository. They ensure unique, sequential values for attributes like customer numbers, document IDs, or any other identifier that requires automatic numbering.

This chapter provides best practices for writing efficient and maintainable GraphQL queries and mutations.

This section provides best practices and recommendations for creating construction kits.

The Construction Kit defines the schema and structure of your data model. Unlike the Runtime Model which stores actual data instances, the Construction Kit contains the metadata that defines what types of entities can exist, their attributes, relationships, and constraints.

GraphQL allows to query and mutate data. Mutations are operations like create, update and delete. This chapter describes how data can be created.

GraphQL allows to query and mutate data. Mutations are operations like create, update and delete. This chapter describes how data can be deleted.

Enums are used for establishing a set of predefined constants, which can represent various states, types, or configurations within the library. Enums are embedded within a Runtime Entity Object and do not need any navigation through associations.

Enums define a fixed set of allowed values for an attribute. Each enum value has a numeric key, a name, and an optional description.

This chapter describes how errors are returned in GraphQL responses and how to handle them.

Fixup Scripts are MongoDB-compatible scripts that can be applied to databases for maintenance, migration, and data correction tasks. They are executed by the bot service in a defined sequence order, ensuring consistent and predictable database modifications.

OctoMesh uses Communication Operators to manage distributed computing resources using Kubernetes. The Communication Operators are responsible for managing the lifecycle of the Adapters, including creating, updating, and deleting Adapters.

In the realm of OctoMesh, adapters and pipelines play a crucial role as the connective tissue between the OctoMesh platform and external data sources and services. These small, but powerful pieces of software are designed to facilitate communication and data exchange across a diverse set of endpoints, including APIs, file systems, databases, message brokers, and other custom or standard protocols. To cater to different architectural needs and deployment scenarios, OctoMesh distinguishes between two main types of adapters: Edge Adapters and Mesh Adapters.

At the heart of OctoMesh lies the concept of Construction Kits. These kits serve as a fundamental building block for defining object models and providing the essential context that transforms data into actionable insights. With OctoMesh, you can construct models that align with your specific needs, allowing you to shape data in ways that make sense for your organization.

Welcome to the OctoMesh Technology Guide, your comprehensive resource for leveraging the transformative power of OctoMesh to architect and manage robust data mesh solutions. This guide is crafted to serve as your navigator through the expansive features of OctoMesh, shedding light on the underlying concepts, providing detailed how-to instructions, and offering practical recipes that help you harness the full potential of your data.

The Maintenance Dashboard allows to get insights about costs and maintenance activities. It provides a comprehensive overview of the maintenance status of the assets and the costs associated with the maintenance activities. The dashboard is designed to help maintenance managers and technicians to monitor the maintenance activities, track the costs, and identify potential issues that require immediate attention.

Models are containers that group related types, attributes, enums, and records. Each model has a name, version, and can depend on other models.

service coreServices(common:meshLogo)[Core Services] in central

Adapters are executing pipelines and pipeline consists of nodes. There are nodes that are common for all adapters and there are nodes that are specific for each adapter. For example the Modbus adapter comes with Modbus nodes, the OPC UA adapter comes with OPC UA nodes, etc.

The integration of OctoMesh with SAP provides a seamless and efficient way to exchange data between the two systems. Leveraging the SAP NetWeaver SDK,

In OctoMesh, data pipelines are integral to the Extract, Transform, Load (ETL) processes that ensure efficient data handling across distributed environments. These pipelines are categorized into Edge Pipelines and Mesh Pipelines, each executed by specific components within the system: Edge Adapters and Mesh Adapters respectively.

Pipeline triggers are used to start the execution of a pipeline based on a cron schedule using the Bot Service.

OctoMesh uses Communication Operators to manage distributed computing resources using Kubernetes. The Communication Operators are responsible for managing the lifecycle of the Adapters, including creating, updating, and deleting Adapters.

In OctoMesh, we understand that data is at the heart of your operations. This chapter focuses on how you can access and interact with your data through our Construction Kits (CK), tailored for both runtime data and stream (time series) data. Leveraging GraphQL endpoints, OctoMesh offers a seamless and efficient way to work with your data, regardless of its nature.

Edge and Mesh Pipelines enable the data flow between the edge and the mesh (cloud) environment. The Edge Pipelines are responsible for preprocessing the data before sending it to the Mesh Pipelines. The Mesh Pipelines are responsible for processing the data in the cloud environment.

OctoMesh is operated using Kubernetes in production. This docs describe a possibility to run OctoMesh on a local docker environment.

Records are composite value types that group related attributes together. Unlike types, records are embedded directly within entities rather than being independent entities with their own runtime IDs.

OctoMesh provides comprehensive backup and restore capabilities for repositories through the octo-cli tool. These

This chapter describes common query patterns for retrieving Construction Kit metadata. The Construction Kit API is read-only - you query the model structure but cannot modify it through GraphQL.

Simple query

Clone the repository to your local machine using:

GraphQL allows to query and mutate data. Mutations are operations like create, update and delete. This chapter describes how data can be created, retrieved, updated and deleted. It provides a reference for the GraphQL scalar types, input types, and enums used in the OctoMesh GraphQL API.

The SearchFilter provides text search capabilities across multiple attributes of an entity. It is optimized for text-based searches and is typically used for search fields in list views.

Start creating libraries

The area `streamData` allows access to stored time series data. Let's start with simple sample that requests the voltage value of all energy meters by their timestamp.

This chapter describes how to create System Queries. For an overview of System Queries and their use cases, see System Queries.

System Queries are reusable query configurations stored in the repository. They allow you to define a query once and execute it from multiple places using only its ID.

This chapter describes how to update System Queries. For an overview of System Queries and their use cases, see System Queries.

Transient queries allow you to dynamically query runtime entities with configurable column paths. Unlike regular queries where the returned fields are defined in the GraphQL query itself, transient queries return data in a table-like structure with rows and cells.

Types define the structure of entities in the Runtime Model. Each type has attributes, can inherit from a base type, and can participate in associations with other types.

GraphQL allows to query and mutate data. Mutations are operations like create, update and delete. This chapter describes how data can be updated.