Tag Archives: GCP

OpenTelemetry’s First Release Candidates

OpenTelemetry has hit another milestone with the tracing specification reaching release candidate status.

With the specification now ready to go, expect to see tracing release candidates of the official APIs and SDKs over the next few weeks, along with updated exporters for Cloud Trace. In the coming months the same will follow for the metrics specification, followed by metrics release candidates of the APIs and SDKs and Cloud Monitoring exporters, followed by the project’s general availability. At this point we’ll switch our default application metrics and distributed tracing instrumentation from OpenCensus to OpenTelemetry.

This is exciting news for Google Cloud customers, as OpenTelemetry will enable even better observability experiences, both with Cloud Monitoring and Cloud Trace, or the third party monitoring and operations tools of your choice.

Originally posted on the on the OpenTelemetry blog.


As we’ve discussed in past announcements, we’re hard at work building OpenTelemetry’s first GA quality release. Today marks another milestone in this journey, with the freezing and first release candidate of the tracing specification.
Tracing Spec Release Candidate

The tracing specification is now considered to be a release candidate (RC) and is frozen, and the OpenTelemetry APIs and SDKs have a stable specification to build their own release candidates against. This means:
  • API, SDK, and Collector release candidates will appear within the next few weeks.
  • No breaking spec changes are allowed between now and the final GA specification, beyond any showstopper (P1) issues that are revealed in the RC period. We don’t expect any of these to appear, but the purpose of the RC period is for us to validate that we have a GA-worthy spec.
  • Some non-breaking changes will be allowed during the RC period. Most of these are clarifications of existing behaviour or are pure editorial updates.
The release candidate sections of the specification include all tracing related dependencies, specifically the following sections: Trace, Baggage, Resource, Context Propagation, Environment Variables, Exporters (for traces). You can view the progress of each OpenTelemetry component’s implementation in the project status matrix.

What’s Coming Next?

Achieving a release candidate of the tracing specification has been the top priority of OpenTelemetry since releasing our beta in March. With this completed, our focus now shifts to tracing release candidates of the APIs, SDKs, Collector, and auto instrumentation components, and producing a release candidate of the metrics specification.

RC Tracing Implementations

Most OpenTelemetry APIs and SDKs are close to completing their tracing RC implementations, and we expect the first wave of these to arrive within the next two weeks. Contributors who are looking to provide instrumentation (for various web frameworks, storage clients, etc.) can start building against release candidate APIs once they arrive. While the APIs may change in response to issues discovered during RC usage and testing (which will result in multiple pre-GA release candidates for these components), these will be extremely constrained.

Several SDKs will have two waves of release candidate milestones: the first will contain functionality from the tracing and context propagation sections of the specification, and the second will include release candidate implementations for baggage, exporters, resources, and environment variables.

Metrics

In parallel to the tracing RC component releases, we will apply the focus that we’ve had on tracing to the metrics specification. Starting this week, we will categorize which work items are required for GA, which can be optionally allowed in GA (non-breaking), and which will be shifted to post-GA. After completing this, we will track our burndown progress, and lock the metrics specification and publish a metrics specification release candidate once all P1 items are complete. Shortly after this, the APIs, SDKs, Collector, and other components will publish release candidates with RC-quality tracing and metrics functionality.

Productionization and GA Readiness Work

Once the metrics specification, SDKs, Collector, and other components reach release candidate status, we will focus on productionization tasks like writing documentation, producing a post-GA versioning strategy, building additional automated tests, etc. Once we are satisfied with each component’s adoptability and reliability, we will announce their general availability.

Overall Timeline

  1. Components (APIs, SDKs, Collector, auto instrumentation, etc.) issue release candidates with RC-quality tracing functionality.
  2. The metrics section of the specification achieves RC quality and is frozen.
  3. Components issue release candidates with RC-quality tracing and metrics functionality.
  4. Once we are satisfied with our metrics + tracing release candidates, OpenTelemetry goes GA.
  5. Logging enters beta, then issues an RC specification, followed by RC-quality logging functionality in each component, followed by a GA for logging.
We will have a better understanding of our GA release timeline in the coming weeks once outstanding work on the metrics specification is fully accounted for.

Tracking a Language’s Progress

As mentioned above, you can view the progress of a particular component (API, SDK, etc.) in the project status matrix. Each component’s implementation has their own timeline, though a core set (the JavaScript, Java, Go, Python, and .Net APIs + SDKs, the Collector, and Java auto instrumentation) are all tracking well. Each component has its own GA burndown board.

FAQ

I want to use OpenTelemetry on my production services; what’s the impact of today’s announcement?

SDKs with release candidate quality tracing support will be available in a few weeks. Release candidates are not recommended for critical production services, however they are functional and are intended to offer APIs that are compatible with their upcoming GA counterparts.

I want to write instrumentation for OpenTelemetry; what’s the impact of today’s announcement?

APIs with release candidate quality tracing support will be available shortly (prior to the SDKs). You can bind against these to produce traces that will be picked up by the OpenTelemetry SDKs or any other implementations that implement the OpenTelemetry APIs.

When will OpenTelemetry offer drop-in replacements for OpenCensus and OpenTracing?

Work is currently underway on bridge APIs that allow OpenTelemetry SDKs to seamlessly replace OpenCensus libraries or OpenTracing implementations. While the delivery date of this functionality is not tied to OpenTelemetry’s GA goals, we expect this to arrive between each API + SDK’s release candidate and GA milestones.

Wrapping Up

Producing a specification release candidate is an important milestone for the OpenTelemetry community, and it took significant effort on the part of our contributors to make this happen. We’d like to thank every person and every organization that was a part of this release, and to recognize that their contributions are laying the groundwork for the project's long term success.

If you haven’t been a part of the OpenTelemetry community but would like to join, now is the perfect time! OpenTelemetry is now in the top three CNCF projects by weekly and cumulative commits, and no matter your level of commitment (ha!) to the project, contributions are always welcome. If you have a particular area that you’re interested in (for example, the Python API + SDK), the best way to get involved is to join the relevant weekly SIG meetings or interact with other contributors on Gitter.

By Morgan McLean, Google Cloud

Recreating historical streetscapes using deep learning and crowdsourcing

For many, gazing at an old photo of a city can evoke feelings of both nostalgia and wonder. We have Google Street View for places in the present day, but what about places in the past? What was it like to walk through Manhattan in the 1940s? To create a rewarding “time travel” experience for both research and entertainment purposes, Google Research is launching Kartta Labs, an open source, scalable system on Google Cloud and Kubernetes that tackles the difficult problem of reconstructing what cities looked like in the past from scarce historical maps and photos.

Kartta Labs consists of three main parts:
  • A temporal map server, which shows how maps change over time;
  • A crowdsourcing platform, which allows users to upload historical maps of cities, georectify, and vectorize them (i.e. match them to real world coordinates);
  • And an upcoming 3D experience platform, which runs on top of maps creating the 3D experience by using deep learning to reconstruct buildings in 3D from limited historical images and maps data.

Maps & Crowdsourcing

Kartta Labs is a growing suite of open source tools that work together to create a map server with a time dimension, allowing users to populate the service with historically accurate data.
gif of editor in use

Warper

The entry point to crowdsourcing is Warper, an open source web app based on MapWarper that allows users to upload historical images of maps and georectify them by finding control points on the historical map and corresponding points on a base map.

Once a user uploads a scanned historical map, Warper makes a best guess of the map’s geolocation by extracting textual information from the map. This initial guess is used to place the map roughly in its location and allow the user to georeference the map pixels by placing pairs of control points on the historical map and a reference map. Given the georeferenced points, the application warps the image such that it aligns well with the reference map.

Warper runs as a Ruby on Rails application using a number of open source geospatial libraries and technologies, including but not limited to PostGIS and GDAL. The resulting maps can be exported in PNG, GeoTIFF, and other open formats. Warper also runs a raster tiles server that serves each georectified map at a tile URL. This raster tile server is used to load the georectified map as a background in the Editor application that is described next.

Editor

Editor is an open source web application which is a customized version of the OpenStreetMap editor; customizations include support for time dimension and integration with the other tools in the Kartta Labs suite. Editor allows users to load the georectified historical maps and trace their geographic features (e.g., building footprints, roads, etc.). This traced data is stored in vector format.

Extracted geometries in vector format, as well as metadata (e.g., address, name, and start or end dates), are stored in a geospatial database that can be queried, edited, styled, and rendered into new maps.

Kartta

Finally, the temporal map front end, Kartta (based on Tegola), visualizes the vector tiles allowing the users to navigate historical maps in space and time. Kartta works like any familiar map application (such as Google Maps), but also has a time slider so the user can choose the year at which they want to see the map. By moving the time slider, the user is able to see how features in the map, such as buildings and roads, changes over time.

3D Experience

To actually create the “time traveling” 3D experience, the forthcoming 3D Models module aims to reconstruct the detailed full 3D structures of historical buildings. The module will associate images with maps data, organize these 3D models properly in one repository, and render them on the historical maps with a time dimension.

Preliminary Results

Figure 2 – Bird’s eye view of 3D-reconstructed  Chelsea, Manhattan with a time slider
Figure 3 – Street level view of 3D-reconstructed Chelsea, Manhattan

Conclusion

We developed the tools outlined above to facilitate crowdsourcing and tackle the main challenge of insufficient historical data. We hope Kartta Labs acts as a nexus for an active community of developers, map enthusiasts, and casual users that not only utilizes our historical datasets and open source code, but actively contributes to both. The launch of our implementation of the Kartta Labs suite is imminent—keep an eye out on the Google AI blog for that announcement!

By Raimondas Kiveris – Google Research

W3C Trace Context Specification: What it Means for You

Since the first days of Google Cloud Platform (GCP), Google has been at the forefront of making your applications more observable. Beyond Stackdriver, our most visible impact in this space is OpenTelemetry, which we initiated in 2017 (as OpenCensus) and has grown into a huge community that includes the majority of APM / monitoring vendors and cloud platforms.

While OpenTelemetry allows developers to easily capture distributed traces and metrics from their own services, there’s also a need to trace requests as they propagate through components that developers don’t directly control, like managed services, load balancers, network hardware, etc. To solve this we co-defined a prototype HTTP header that these components can rely on, gathered partners, and moved the work into the W3C.

This work is now complete, and the W3C Trace Context format is now an official standard. Once implemented in GCP, this will make our services even easier to manage, both with Stackdriver and other third party distributed tracing tools. We explain more in the
official post on the W3C blog, which I’ve copied below:

The W3C Distributed Tracing working group has moved the Trace Context specification to the next maturity level. The specification is already being adopted and implemented by many platforms and SDKs. This article describes the Trace Context specification and how it improves troubleshooting and monitoring of modern distributed apps.

W3C Trace Context specification defines the format for propagating distributed tracing context between services. Distributed tracing makes it easy for developers to find the causes of issues in highly-distributed microservices applications by tracking how a single interaction was processed across multiple services. Each step of a trace is correlated through an ID that is passed between services, and W3C Trace Context now defines a standard for these context propagation headers.

Until now, different tracing systems have defined their own headers. Examples include Zipkin’s B3 format and X-Google-Cloud-Trace. Adopting a common context propagation format has been long desired by developers, APM vendors, and cloud platform hosts, as compatibility provides numerous benefits:
  • Web and RPC frameworks that use this standard to provide context propagation out of the box will also offer cross-service log correlation, even for developers who haven’t set up distributed tracing.
  • API producers can record the trace IDs of requests from API consumers and provide additional spans or metadata to their customers for a given traced request. Producers can also correlate customer trace IDs to internal traces when debugging technical issues raised by consumers.
  • Networking infrastructure (proxies, load balancers, routers, etc.) can both ensure that context propagation headers are not removed from requests passing through them, and can record spans or logs for a given trace, without having to support multiple vendor-specific formats. Potential examples of these include router appliances, cloud load balancers, and sidecar proxies like Envoy.
  • Instrumentation can be further decoupled from a developer’s choice of APM vendor. For example, using both OpenTelemetry and a given vendor’s agents, a developer can instrument different services in an application, and traces will flow through the system and be processed correctly by the vendor’s backend.
  • Web browsers and other clients can use these identifiers to correlate their telemetry with traces collected from backend services. This functionality is currently being defined.
To address this effort, a group of cloud providers, open source contributors, and APM vendors started defining a standard HTTP context propagation header that would replace their homegrown formats. This specification has been discussed and iterated on over the past two years, and the group working on it has grown significantly over that time. Sponsors include Google, Microsoft, Dynatrace, and New Relic (W3C members), and the group was officially moved into the W3C in 2018 for the work to proceed under the guidance of an official standards body and to spur even greater adoption.

TraceContext has since been adopted by OpenTelemetry (which enables it by default and also serves as the reference implementation), Azure services, Dynatrace, Elastic, Google Cloud Platform, Lightstep, and New Relic. We are tracking adoption in this list.

This first phase of work has focused on HTTP, as it is commonly used and has no built-in affordances for trace context propagation (gRPC and some newer RPC systems do). The same group of committee members are also working to define trace context propagation in other formats, starting with AMQP and MQTT for IoT; other upcoming topics include context propagation from clients and web browsers.

By Morgan McLean, OpenTelemetry + Stackdriver

A new issue tracker for G Suite developers

Originally Posted on the G Suite Developers Blog
Posted by Ryan Roth, Developer Programs Engineer & Wesley Chun, Developer Advocate, G Suite

You may have read recently that the Google Cloud Platform team upgraded to Issue Tracker, the same system that Google uses internally. This allows for improved collaboration between all of us and all of you. Issues you file will have better exposure internally, and you get improved transparency in terms of seeing the issues we're actively working on. Starting today, G Suite developers will also have a new issue tracker to which we've already migrated existing issues from previous systems.

Whether it's a bug that you've found, or if you wish to submit a favorite feature request, the new issue tracker is here for you. Heads up, you need to be logged in with your Google credentials to view or update issues in the tracker.



The new issue tracker for G Suite developers.

Each G Suite API and developer tool has its own "component" number that you can search. For your convenience, below is the entire list. You may browse for issues relevant to the Google APIs that you're using, or click on the convenience links to report an issue or request a new/missing feature:
To get started, take a look at the documentation pages, as well as the FAQ. For more details, be sure to check out the Google Cloud Platform announcement, too. We look forward to working more closely with all of you soon!