Construct an finish to finish JSON logging system for shoppers apps | by Pinterest Engineering | Pinterest Engineering Weblog

Liang Ma | Software program Engineer, Core Eng; Wei Zhu | Software program Engineer, Observability

Flow map: Pinterest app to JSON logs batch to Logservice — a batch logging endpoint (/log) that handles perf logs, device info(Android) and new JSON log type to json messages to Singer to Pub/Sub with arrows to Logstash, Merced and Other analytics tools. Logstash goes to Open Search. Open Search goes to OpenSearch Dashboards and Metric generator to Statsboard. Merced goes to S3/Hive.

In early 2020, throughout a vital iOS out of reminiscence incident (we’ve got a blogpost for that), we realized that we didn’t have a lot visibility of how the app is operating or a very good system to lookup for monitoring and troubleshooting.

At the moment, on the consumer facet, there have been a number of methods for logging of their each day work:

Context logging: constructed for logging and reporting impressions or something associated to enterprise, thus a time vital and first-class endpoint. Builders have to explicitly outline keys that will in any other case be rejected by the endpoint. Some corporations name it “analytics logging.”
Misc: logging to a neighborhood file on disk, and even logging to a crash monitoring service as an error kind.

The issues are:

Not all logs fall into these classes, and folks typically abuse sure sorts of logging
None of those instruments present a great way to visualise or combination. For instance, builders have to make code adjustments to populate data like “what the metric appears like on app model A, on gadget B, and beneath community kind C”
There isn’t a system that may simply monitor logs in a real-time means, to not point out arrange real-time alerts with log-based customized metrics.

We determined to create an end-to-end pipeline with the next traits:

It’s constructed with the least resistance: log payload is schemaless and versatile, principally key-value pairs. That’s one of many causes we name it JSON logging.
It’s prepared to make use of logging APIs on every platform
Builders don’t want to the touch any backend stuff
It’s simple to question and visualize logs
Performs in real-time!

With these in thoughts, the next key design choices have been made:

The logging service endpoint will deal with logs validating, parsing, and processing.
Logs can be continued in hive, thus supporting any SQL-based queries.
A single and shared Kafka subject can be used for all logs going by way of this pipeline.
It’s built-in with OpenSearch (Amazon’s fork of Elasticsearch and Kibana) as an actual time visualization and question instrument.
It will likely be simple to arrange real-time alerting with log-based customized metrics.

Table of Contents

Excessive stage

Schema

Consumer facet service integration will present the metadata, and builders simply want to supply the title of the log and precise log payload. Nothing else is required.

A pattern payload

“name” = “network_metrics”; //required, set by users “timestamp” = 2022121512345; //required, set by pipeline “metadata” = //required, set by pipeline “app_version” = “8.40”; “os_version” = “14.0”; “device_model” = “IPHONE11,2; “build_type” = “Production” // “OTA”, “Development”, “Alpha”, etc “network_type” = “wifi” // or “cellular” “country” = “United States”; “platform” = “Android”; … ; “payload” = // users reported payload will appear here ; ;

Visualize and question

Visualization of logs on Opensearch is comparatively easy following the self-service steerage offered for this pipeline. Additionally, builders can use SQL question and every other question/visualization instruments which are supported by this pipeline to question.

Example on how to visualize network metrics in real-time with six separate graphics: mobile_json)log::platforms, mobile_networking::host, mobile_json_log::total_count_timeline, mobile_networking::req_num_by_ver, mobile_networking::request_latency, and mobile_networking::status. — Determine 2 — a pattern dashboard of community logs from each iOS and Android apps

Actual-time alerting

Log-based metrics are a cost-efficient technique to summarize log information from your complete ingest stream. With log-based metrics, customers can generate a rely metric of logs that match a Lucene question. For extra superior use instances, customers can generate metrics from an OpenSearch time period aggregation question to dissect log information throughout completely different dimensions.

Example on how to create a log-based metric. “Succeeded. Metric Name: es.mobile_json.story_pin_by_event_type. Query Name: name: story_pin_creation_event AND metadata.build_type:Production. Index Name: mobile_json_log. Begin: -30mins. End: -5min. Term Aggs (optional) Field: payload.eventType.key. Tag Key: event_type. Size: 10. Order: desc. Field: metadata.platform.key. Tag Key: platform. Size: 10. Order: desc.” — Determine 3 — instance: tips on how to create a log-based metric

Log-based metrics can be utilized to construct dashboards and real-time alerts:

Title of Tab: ES Mobile JSON Story Pin Event. sum_aggregator: zimsum:1m-avg-none. Two Stratsboards with red lines and dots titled “iOS story pin event SR” and “Android story pin event SR”. — Determine 4 — instance: a real-time alerting arrange based mostly on the log-based metric, on Statsboard

Since this pipeline was constructed up with none actual push, builders have been proactively adopting this logging system primarily for:

Consumer visibility

Networking metrics and crash metrics so that they know higher how the shoppers carry out and get that consumer facet indicators to the topline Pinner Uptime metric
Efficiency perception, reminiscent of data offered by iOS MetricKit
Customized error reporting, reminiscent of exceptions, gentle errors, and assertions that have been beforehand both not reported or reported someplace and didn’t have a very good instrument to research

Product floor/function SLA

Some product groups leverage this technique to report product function well being, reminiscent of Pin creation outcomes, to allow them to monitor success/failure charges in real-time. This typically catches points means sooner than the same old each day metric aggregation, and it’s particularly helpful for points that API facet monitoring wouldn’t alert immediately.

Developer logs

Builders like to make use of this pipeline to realize visibility of sure logic or code paths on manufacturing, e.g. “has this code ever run?,”, “how typically does this occur?”, and plenty of comparable questions that nobody can reply besides the information.
Builders add logs to assist troubleshoot odd bugs which are very onerous to breed regionally or points that solely happen on sure gadget fashions, OS variations, and many others.

Actual Time alerting

Due to the convenience of reporting and alerting setup, product groups typically use that only for the sake of real-time alerting.

On the Opensearch facet, create sub-level indexes by title, which may increase question efficiency and in addition higher isolate logs
Discover the alerting perform offered by Opensearch

Acknowledgements: large because of Stephen Blanco, Darren Gyles, Sha Sha Chu, Nadine Harik, Roger Wang, and our information & infra workforce for his or her contribution, suggestions and help.

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