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Real-time reconciliation with Overseer | by Coinbase | Sep, 2022

Tl;dr: A standard problem with distributed programs is how to make sure that state stays synchronized throughout programs. At Coinbase, this is a crucial drawback for us as many transactions circulation by our microservices on daily basis and we have to be certain that these programs agree on a given transaction. In this weblog put up, we’ll deep-dive into Overseer, the system Coinbase created to supply us with the power to carry out real-time reconciliation.

By Cedric Cordenier, Senior Software Engineer

Every day, transactions are processed by Coinbase’s funds infrastructure. Processing every of those transactions efficiently means finishing a fancy workflow involving a number of microservices. These microservices vary from “front-office” companies, such because the product frontend and backend, to “back-office” companies comparable to our inner ledger, to the programs answerable for interacting with our banking companions or executing the transaction on chain.

All of the programs concerned in processing a transaction retailer some state regarding it, and we have to be certain that they agree on what occurred to the transaction. To remedy this coordination drawback, we use orchestration engines like Cadence and methods comparable to retries and idempotency to make sure that the transactions are ultimately executed appropriately.

Despite this effort, the programs often disagree on what occurred, stopping the transaction from finishing. The causes of this blockage are different, starting from bugs to outages affecting the programs concerned in processing. Historically, unblocking these transactions has concerned important operational toil, and our infrastructure to deal with this drawback has been imperfect.

In explicit, our programs have lacked an exhaustive and immutable document of the entire actions taken when processing a transaction, together with actions taken throughout incident remediation, and been unable to confirm the consistency of a transaction holistically throughout the complete vary of programs concerned in actual time. Our present course of relied on ETL pipelines which meant delays of as much as 24 hours to have the ability to entry current transaction information.

To remedy this drawback, we created Overseer, a system to carry out close to real-time reconciliation of distributed programs. Overseer has been designed with the next in thoughts:

  • Extensibility: Writing a brand new verify is so simple as writing a operate, and including a brand new information supply is a matter of configuration within the common case. This makes it straightforward for brand spanking new groups to onboard checks onto the platform that’s Overseer.
  • Scalability: As of as we speak, our inner metrics present that Overseer is able to dealing with greater than 30k messages per second.
  • Accuracy: Overseer travels by time and intelligently delays operating a verify for a short while to compensate for delays in receiving information, thus lowering the variety of false negatives.
  • Near real-time: Overseer has a time to detect (TTD) of lower than 1 minute on common.


At a high-level, the structure of Overseer consists of the three companies pictured above:

  • The ingestion service is how any new information enters Overseer. The service is answerable for receiving replace notifications from the databases which Overseer is subscribed, storing the replace in S3, and notifying the upstream processors runner service (PRS) of the replace.
  • The information entry layer service (DAL) is how companies entry the info saved in S3. Each replace is saved as a single, immutable, object in S3 and the DAL is answerable for aggregating the updates right into a canonical view of a document at a given cut-off date. This additionally serves because the semantic layer on high of S3 by translating information from its at-rest illustration — which makes no assumptions in regards to the schema or format of the info — into protobufs, and by defining the be part of relationships essential to sew a number of associated data into a knowledge view.
  • The processors runner service (PRS) receives these notifications and determines which checks — often known as processors — are relevant to the notification. Before operating the verify, it calls the information entry layer service to fetch the info view required to carry out the verify.

The Ingestion Service

A predominant design objective of the ingestion service is to assist any format of incoming information. As we glance to combine Overseer into all of Coinbase programs sooner or later, it’s essential that the platform is constructed to simply and effectively add new information sources.

Our typical sample for receiving occasions from upstream information sources is to tail its database’s WAL (write-ahead log). We selected this method for a number of causes:

  • Coinbase has a small variety of database applied sciences which can be thought-about “paved road”, so by supporting the info format emitted by the WAL, we will make it straightforward to onboard nearly all of our companies.
  • Tailing the WAL additionally ensures a excessive degree of knowledge constancy as we’re replicating straight what’s within the database. This eliminates a category of errors which the choice — to have upstream information sources emit change occasions on the software degree — would expose us to.

The ingestion service is ready to assist any information format on account of how information is saved and later acquired. When the ingestion service receives an replace, it creates two artifacts — the replace doc and the grasp doc.

  • The replace doc accommodates the replace occasion precisely as we acquired it from the upstream supply, in its unique format (protobuf bytes, JSON, BSON, and so forth) and provides metadata such because the distinctive identifier for the document being modified.
  • The grasp doc aggregates the entire references present in updates belonging to a single database mannequin. Together, these paperwork function an index Overseer can use to affix data collectively.

When the ingestion service receives an replace for a document, it extracts these references and both creates a grasp doc with the references (if the occasion is an insert occasion), or updates an present grasp doc with any new references (if the occasion is an replace occasion). In different phrases, ingesting a brand new information format is only a matter of storing the uncooked occasion and extracting its metadata, such because the document identifier, or any references it has to different data.

To obtain this, the ingestion service has the idea of a client abstraction. Consumers translate a given enter format into the 2 artifacts we talked about above and may onboard new information sources, by configuration, to tie the info supply to a client to make use of at runtime.

However, this is only one a part of the equation. The potential to retailer arbitrary information is simply helpful if we will later retrieve it and provides it some semantic which means. This is the place the Data Access Layer (DAL) is helpful.

DAL, Overseer’s semantic layer

To perceive the function performed by DAL, let’s look at a typical replace occasion from the attitude of a hypothetical Toy mannequin, which has the schema described beneath:

kind Toy struct {
Type string
Color string
Id string

We’ll additional assume that our Toy mannequin is hosted in a MongoDB assortment, such that change occasions can have the uncooked format described here. For our instance Toy document, we’ve recorded two occasions, specifically an occasion creating it, and a subsequent replace. The first occasion appears to be like roughly like this, with some irrelevant particulars or discipline elided:

"_id": "22914ec8-4687-4428-8cab-e0fd21c6b3b6",
"fullDocument": {
"type": "watergun",
"color": "blue",
"clusterTime": 1658224073,

And, the second, like this:

"_id": "22914ec8-4687-4428-8cab-e0fd21c6b3b6",
"updateDescription": {
"updatedFields": {
"type": "balloon",
"clusterTime": 1658224074,

We talked about earlier that DAL serves because the semantic layer on high of Overseer’s storage. This means it performs three features with respect to this information:

Time journey: retrieving the updates belonging to a document as much as a given timestamp. In our instance, this might imply retrieving both the primary or each of those updates.

Aggregation: remodeling the updates right into a view of the document at a cut-off date, and serializing this into DAL’s output format, protobufs.

In our case, the updates above will be remodeled to explain the document at two cut-off dates, specifically after the primary replace, and after the second replace. If we have been focused on understanding what the document appeared like on creation, we might remodel the updates by fetching the primary replace’s “fullDocument” discipline. This would end result within the following:

Type: "watergun",
Id: "22914ec8-4687-4428-8cab-e0fd21c6b3b6",
Color: "blue",

However, if we wished to know what the document would appear like after the second replace, we might as a substitute take the “fullDocument” of the preliminary replace and apply the contents of the “updateDescription” discipline of subsequent updates. This would yield:

Type: "balloon",
Id: "22914ec8-4687-4428-8cab-e0fd21c6b3b6",
Color: "blue",

This instance accommodates two essential insights:

  • First, the algorithm required to combination updates relies on the enter format of the info. Accordingly, DAL encapsulates the aggregation logic for every kind of enter information, and has aggregators (referred to as “builders”) for the entire codecs we assist, comparable to Mongo or Postgres for instance.
  • Second, aggregating updates is a stateless course of. In an earlier model of Overseer, the ingestion service was answerable for producing the newest state of a mannequin along with storing the uncooked replace occasion. This was performant however led to considerably lowered developer velocity, since any errors in our aggregators required a expensive backfill to right.

Exposing information views

Checks operating in Overseer function on arbitrary information views. Depending on the wants of the verify being carried out, these views can comprise a single document or a number of data joined collectively. In the latter case, DAL gives the power to determine sibling data by querying the gathering of grasp data constructed by the ingestion service.

PRS, a platform for operating checks

As we talked about beforehand, Overseer was designed to be simply extensible, and nowhere is that this extra essential than within the design of the PRS. From the outset, our design objective was to make including a brand new verify as straightforward as writing a operate, whereas retaining the flexibleness to deal with the number of use instances Overseer was supposed to serve.

A verify is any operate which performs the next two features:

  1. It makes assertions when given information. A verify can declare which information it wants by accepting a knowledge view offered by DAL as a operate argument.
  2. It specifies an escalation coverage: i.e. given a failing assertion, it comes to a decision on the best way to proceed. This could possibly be so simple as emitting a log, or creating an incident in PagerDuty, or performing every other motion determined by the proprietor of the verify.

Keeping checks this easy facilitates onboarding — testing is especially straightforward as a verify is only a operate which accepts some inputs and emits some unwanted effects — however requires PRS to deal with lots of complexity routinely. To perceive this complexity, it’s useful to realize an summary of the lifecycle of an replace notification inside Overseer. In the structure overview initially of this put up, we noticed how updates are saved by the ingestion service in S3 and the way the ingestion service emits a notification to PRS through an occasions subject. Once a message has been acquired by PRS, it goes by the next circulation:

  • Selection: PRS determines which checks must be triggered by the given occasion.
  • Scheduling: PRS determines when and the way a verify must be scheduled. This occurs through what we name “execution strategies”. These can are available in varied kinds, however fundamental execution methods may execute a verify instantly (i.e. do nothing), or delay a verify by a set period of time, which will be helpful for implementing SLAs. The default execution technique is extra complicated. It drives down the speed of false negatives by figuring out the relative freshness of the info sources that Overseer listens to, and will select to delay a verify — thus sacrificing a bit of little bit of our TTD — to permit lagging sources to catch up.
  • Translation maps the occasion acquired to a particular information view required by the verify. During this step, PRS queries the DAL to fetch the data wanted to carry out the verify.
  • Finally, execution, which calls the verify code.

Checks are registered with the framework by a light-weight domain-specific language (DSL). This DSL makes it doable to register a verify in a single line of code, with wise defaults specifying the conduct when it comes to what ought to set off a verify (the choice stage), the best way to schedule a verify, and what view it requires (the interpretation stage). For extra superior use instances, the DSL additionally acts as an escape hatch by permitting customers to customise the conduct of their verify at every of those phases.

Today, Overseer processes greater than 30,000 messages per second, and helps 4 separate use instances in manufacturing, with a objective so as to add two extra by the tip of Q3. This is a major milestone for the undertaking which has been in incubation for greater than a 12 months, and required overcoming quite a lot of technical challenges, and a number of modifications to Overseer’s structure.

This undertaking has been a real staff effort, and wouldn’t have been doable with out the assistance and assist of the Financial Hub product and engineering management, and members of the Financial Hub Transfers and Transaction Intelligence groups.

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