A Guide to Using OpenTelemetry in Jina for Monitoring and Tracing Applications

• docarray

  32 2,730 9.2 Python

  Represent, send, store and search multimodal data

  

DocArray to manipulate data and interact with the storage backend using document store.

• opentelemetry-collector-contrib

  43 2,521 10.0 Go

  Contrib repository for the OpenTelemetry Collector

  

Right now, Jina only supports the export mechanism to push telemetry signals to external systems. It uses OpenTelemetry Collector Contrib as the unified component to collect telemetry signals before exporting to enhanced components that transform the data for visualization and analysis. The collector setup is very basic and functions only as the uniform intermediary for collecting and exporting data.

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• opentelemetry-go

  126 4,752 9.6 Go

  OpenTelemetry Go API and SDK

  

OpenTelemetry is a project incubated by CNCF. It brings distributed tracing and metrics. Pamela has brought us on to implement this telemetry into her Jina Flow. Before getting started we have to learn a few new terms:

• now

  8 588 9.7 Python

  Discontinued 🧞 No-code tool for creating a neural search solution in minutes (by jina-ai)

  

💡In this post we’re just building out a backend, and not touching on a frontend. To build your own low-code backend+frontend neural search solution, check out Jina NOW.

• self-hosted

  28 7,226 9.0 Shell

  Sentry, feature-complete and packaged up for low-volume deployments and proofs-of-concept

  

We use the self-hosted Sentry application landscape to set up the actual APM or SPM. We'll only explore a small set of features supported by Sentry to preserve the focus of this post. Refer to the documentation for more details.

• finetuner

  36 1,423 5.5 Python

  :dart: Task-oriented embedding tuning for BERT, CLIP, etc.

  

We derived the dataset by pre-processing the deepfashion dataset using Finetuner. The image label generated by Finetuner is extracted and formatted to produce the text attribute of each product.

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