Snapshot SQL engine

Kafka SQL pull with snapshot queries

The Lenses SQL Snapshot engine accesses the data at the point in time the query is executed. This means, for Apache Kafka, data added just after the query was initiated will not be processed.

Typical use cases are, but limited to:

  • Identifying a specific message.
  • Identifying a specific payment transaction which your system has processed
  • Identifying all the thermostats readings for a specific customer if you are working for an energy provider
  • Counting transactions processed within a given time window.

What is a message? 

A single entry in a Kafka topic is called a message.

The engine considers a message to have four distinct components key, value, headers and metadata.


Currently, the Snapshot Engine supports four different facets _key, _value, _headers and _metadata; These strings can be used to reference properties of each of the aforementioned message components and build a query that way.

By default, unqualified properties are assumed to belong to the _value facet:

FROM source_topic;

In order to reference a different facet, a facet qualifier can be added:

FROM source_topic;

When more than one sources/topics are specified in a query (like it happens when two topics are joined) a table reference can be added to the selection to fix the ambiguity:

FROM users JOIN purchases

the same can be done for any of the other facets (_key,_meta,_headers).

Selecting nested fields 

Messages can contain nested elements and embedded arrays. The . operator is used to refer to children, and the [] operator is used for referring to an element in an array.

You can use a combination of these two operators to access data of any depth.

    dependencies[0].first_name AS childName
FROM policy_holder
WHERE policyId='100001'

You explicitly reference the key, value and metadata.

For the key use _key, for the value use _value, and for metadata use _meta. When there is no prefix, the engine will resolve the field(s) as being part of the message value. For example, the following two queries are identical:

FROM payments;

FROM payments;

Primitive types 

When the key or a value content is a primitive data type use the prefix only to address them.

For example, if messages contain a device identifier as the key and the temperature as the value, SQL code would be:

    _key AS deviceId
  , _value AS temperature
FROM iot_data

Accessing metadata 

Use the _meta keyword to address the metadata. For example:

    _meta.timestamp AS timestamp
    , _meta.offset AS index
FROM iot_data

Projections and nested aliases 

When projecting a field into a target record, Lenses allows complex structures to be built. This can be done by using a nested alias like below:

    amount as user.amount
    userId as
FROM payments;

The result would be a struct with the following shape:

  "user": {
    "amount" : 10.19,
    "id": 10

Alias clashes (repeated fields) 

When two alias names clash, the snapshot engine does not “override” that field. Lenses will instead generate a new name by appending a unique integer. This means that a query like the following:

    amount as result.amount,
    amount + 5 as result.amount
FROM payments;

will generate a structure like the following:

  "result": {
    "amount" : 10, 
    "amount0": 15