Knowledge ingestion pipeline with Operation Administration (Marken)

At Netflix, to advertise and suggest the content material to customers in the very best manner there are numerous Media Algorithm groups which work hand in hand with content material creators and editors. A number of of those algorithms intention to enhance completely different handbook workflows in order that we present the customized promotional picture, trailer or the present to the person.

These media targeted machine studying algorithms in addition to different groups generate a number of knowledge from the media information, which we described in our earlier weblog, are saved as annotations in Marken. We designed a singular idea referred to as Annotation Operations which permits groups to create knowledge pipelines and simply write annotations with out worrying about entry patterns of their knowledge from completely different purposes.

Annotation Operations

Lets decide an instance use case of figuring out objects (like bushes, automobiles and so forth.) in a video file. As described within the above image

  • In the course of the first run of the algorithm it recognized 500 objects in a specific Video file. These 500 objects have been saved as annotations of a particular schema kind, let’s say Objects, in Marken.
  • The Algorithm crew improved their algorithm. Now after we re-ran the algorithm on the identical video file it created 600 annotations of schema kind Objects and saved them in our service.

Discover that we can’t replace the annotations from earlier runs as a result of we don’t know what number of annotations a brand new algorithm run will consequence into. Additionally it is very costly for us to maintain observe of which annotation must be up to date.

The objective is that when the patron comes and searches for annotations of kind Objects for the given video file then the next ought to occur.

  • Earlier than Algo run 1, in the event that they search they need to not discover something.
  • After the completion of Algo run 1, the question ought to discover the primary set of 500 annotations.
  • In the course of the time when Algo run 2 was creating the set of 600 annotations, purchasers search ought to nonetheless return the older 500 annotations.
  • When the entire 600 annotations are efficiently created, they need to exchange the older set of 500.
  • So now when purchasers search annotations for Objects then they need to get 600 annotations.

Does this remind you of one thing? This appears very related (not precisely similar) to a distributed transaction.

Sometimes, an algorithm run can have 2k-5k annotations. There are a lot of naive options attainable for this downside for instance:

  • Write completely different runs in several databases. That is clearly very costly.
  • Write algo runs into information. However we can’t search or current low latency retrievals from information
  • And many others.

As a substitute our problem was to implement this characteristic on high of Cassandra and ElasticSearch databases as a result of that’s what Marken makes use of. The answer which we current on this weblog is just not restricted to annotations and can be utilized for another area which makes use of ES and Cassandra as properly.

Marken’s structure diagram is as follows. We refer the reader to our earlier weblog article for particulars. We use Cassandra as a supply of reality the place we retailer the annotations whereas we index annotations in ElasticSearch to offer wealthy search functionalities.

Marken Structure

Our objective was to assist groups at Netflix to create knowledge pipelines with out desirous about how that knowledge is on the market to the readers or the shopper groups. Equally, shopper groups don’t have to fret about when or how the info is written. That is what we name decoupling producer flows from purchasers of the info.

Lifecycle of a film goes by way of a number of inventive levels. Now we have many short-term information that are delivered earlier than we get to the ultimate file of the film. Equally, a film has many alternative languages and every of these languages can have completely different information delivered. Groups usually wish to run algorithms and create annotations utilizing all these media information.

Since algorithms could be run on a unique permutations of how the media information are created and delivered we are able to simplify an algorithm run as follows

  • Annotation Schema Kind — identifies the schema for the annotation generated by the Algorithm.
  • Annotation Schema Model — identifies the schema model of the annotation generated by the Algorithm.
  • PivotId — a singular string identifier which identifies the file or methodology which is used to generate the annotations. This could possibly be the SHA hash of the file or just the film Identifier quantity.

Given above we are able to describe the info mannequin for an annotation operation as follows.

"annotationOperationKeys": [

"annotationType": "string", ❶
"annotationTypeVersion": “integer”,
"pivotId": "string",
"operationNumber": “integer” ❷

"id": "UUID",
"operationStatus": "STARTED", ❸
"isActive": true ❹
  1. We already defined AnnotationType, AnnotationTypeVersion and PivotId above.
  2. OperationNumber is an auto incremented quantity for every new operation.
  3. OperationStatus — An operation goes by way of three phases, Began, Completed and Canceled.
  4. IsActive — Whether or not an operation and its related annotations are energetic and searchable.

As you possibly can see from the info mannequin that the producer of an annotation has to decide on an AnnotationOperationKey which lets them outline how they need UPSERT annotations in an AnnotationOperation. Inside, AnnotationOperationKey the necessary subject is pivotId and the way it’s generated.

Our supply of reality for all objects in Marken in Cassandra. To retailer Annotation Operations we’ve the next foremost tables.

  • AnnotationOperationById — It shops the AnnotationOperations
  • AnnotationIdByAnnotationOperationId — it shops the Ids of all annotations in an operation.

Since Cassandra is NoSql, we’ve extra tables which assist us create reverse indices and run admin jobs in order that we are able to scan all annotation operations at any time when there’s a want.

Every annotation in Marken can also be listed in ElasticSearch for powering varied searches. To report the connection between annotation and operation we additionally index two fields

  • annotationOperationId — The ID of the operation to which this annotation belongs
  • isAnnotationOperationActive — Whether or not the operation is in an ACTIVE state.

We offer three APIs to our customers. In following sections we describe the APIs and the state administration accomplished inside the APIs.


When this API is known as we retailer the operation with its OperationKey (tuple of annotationType, annotationType Model and pivotId) in our database. This new operation is marked to be in STARTED state. We retailer all OperationIDs that are in STARTED state in a distributed cache (EVCache) for quick entry throughout searches.



Customers name this API to upsert the annotations in an Operation. They move annotations together with the OperationID. We retailer the annotations and in addition report the connection between the annotation IDs and the Operation ID in Cassandra. Throughout this section operations are in isAnnotationOperationActive = ACTIVE and operationStatus = STARTED state.

Be aware that sometimes in a single operation run there could be 2K to 5k annotations which could be created. Purchasers can name this API from many alternative machines or threads for quick upserts.



As soon as the annotations have been created in an operation purchasers name FinishAnnotationOperation which modifications following

  • Marks the present operation (let’s say with ID2) to be operationStatus = FINISHED and isAnnotationOperationActive=ACTIVE.
  • We take away the ID2 from the Memcache since it isn’t in STARTED state.
  • Any earlier operation (let’s say with ID1) which was ACTIVE is now marked isAnnotationOperationActive=FALSE in Cassandra.
  • Lastly, we name updateByQuery API in ElasticSearch. This API finds all Elasticsearch paperwork with ID1 and marks isAnnotationOperationActive=FALSE.

Search API

That is the important thing half for our readers. When a shopper calls our search API we should exclude

  • any annotations that are from isAnnotationOperationActive=FALSE operations or
  • for which Annotation operations are presently in STARTED state. We do this by excluding the next from all queries in our system.

To attain above

  1. We add a filter in our ES question to exclude isAnnotationOperationStatus is FALSE.
  2. We question EVCache to seek out out all operations that are in STARTED state. Then we exclude all these annotations with annotationId present in memcache. Utilizing memcache permits us to maintain latencies for our search low (most of our queries are lower than 100ms).

Cassandra is our supply of reality so if an error occurs we fail the shopper name. Nevertheless, as soon as we decide to Cassandra we should deal with Elasticsearch errors. In our expertise, all errors have occurred when the Elasticsearch database is having some situation. Within the above case, we created a retry logic for updateByQuery calls to ElasticSearch. If the decision fails we push a message to SQS so we are able to retry in an automatic style after some interval.

In close to time period, we wish to write a excessive stage abstraction single API which could be referred to as by our purchasers as a substitute of calling three APIs. For instance, they’ll retailer the annotations in a blob storage like S3 and provides us a hyperlink to the file as a part of the only API.