Overwatch was built to enable Databricks’ customers, employees, and partners to quickly / easily understand operations within Databricks deployments. As enterprise adoption increases there’s an ever-growing need for strong governance. Overwatch means to enable users to quickly answer questions and then drill down to make effective operational changes. Common examples of operational activities Overwatch assists with are:
Overwatch is currently a Databricks Labs project and has no direct cost associated with it other than the costs incurred with running the daily jobs. Audit logging is required and as such a Azure Databricks Premium SKU OR the equivalent AWS Premium Plan or above. As a reference, a cost analysis was performed at a large Databricks customer. This customer had >1000 named users with >400 daily active users with a contract price with Databricks over $2MM/year. Overwatch costs them approximately $8 USD / day to run which includes storage, compute, and DBUs. $8 / day == approximately $3,000 / year or 0.15% of Databricks contract price. This is just a single reference customer but cost monitoring for your Overwatch job should be the same as any other job you run. At the time of this writing Overwatch has only been deployed as a pilot and has already proven several million dollars in value. The savings customers found through these efficiency improvements allowed them to accelerate their backlogs, permanently lower the total cost of ownership, and even take on additional exploratory efforts.
Overwatch has two primary modes, Historical & Realtime
Overwatch amalgamates and unifies all the logs produced by Spark and Databricks via a periodic job run (typically 1x/day). The Overwatch job then enriches this data through various API calls to the Databricks platform and, in some cases, the cloud provider. Finally, all the data is pulled together into a data model that enables simplified queries for powerful insights and governance. Below are a few example reports produced by Overwatch. The batch / historical data is typically used to identify the proper boundaries that can later be used to set up monitoring / alerts in the real-time modules.
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A supported TSDB (time-series database) is required to enable and integrate this. Databricks cannot provide support for deployment / configuration of TSDBs but can assist in integrating Databricks clusters with supported TSDBs. Supported TSDBs include:
Overwatch is often integrated with real-time solutions to enhance the data provided as raw Spark Metrics. For example, you may be monitoring jobs in real-time but want job_names instead of job_ids, Overwatch’s slow-changing dimensions can enhance this.
Real-time monitoring usually comes from at least two different sources:
These real-time metrics can be captured as quickly as 5s intervals but it’s critical to note that proper historization is a must for higher intervals; furthermore, it’s critical to configure the metrics at the “correct” interval for your business needs. In other words, it can get quickly get expensive to load all metrics at 5s intervals. All of these details are likely common knowledge to a team that manages a time-series database (TSDB).
Below is a scalable, reference architecture using Graphite and Grafana to capture these metrics. Overwatch creates several daily JSON time-series compatible exports to Grafana JSON that provide slow-changing dimensional lookups between real-time keys and dimensional values through joins for enhanced dashboards.
Below is a link to a notebook offering samples for integrating Spark and machine metrics to some real-time infrastructure endpoint. The examples in this notebook offer examples to Prometheus, Graphite, and Log Analytics for Spark Metrics and collectD for machine metrics. Critical this is just a sample for review, this notebook is intended as a reference to guide you to creating your own implementation, you must create a script to be valid for your requirements and capture the right metrics and the right intervals for the namespaces from which you wish to capture metrics.
The realtime reference architecture has been validated but 1-click delivery has not yet been enabled. The time-series database / infrastructure setup is the responsibility of the customer; Databricks can assist with integrating the spark metrics delivery with customer infrastructure but Databricks cannot offer much depth for standing up / configuring the real-time infrastructure itself.
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