Concepts
This page describes the concepts of the Lenses SQL snapshot engine that drives the SQL Studio allowing you to query data in Kafka.
Snapshot queries on streaming data provide answers to a direct question, e.g. The current balance is $10. The query is active, the data is passive.
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
.
Facets
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:
SELECT
property
FROM source_topic;
In order to reference a different facet, a facet qualifier can be added:
SELECT
_value.valueField,
_key.keyField,
_meta.metaField,
_headers.headerField
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:
SELECT
users._value.field
FROM users JOIN purchases
the same can be done for any of the other facets (_key
,_meta
,_headers
).
Note Using a wildcard selection statement SELECT * provides only the value component of a message.
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.
SELECT
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:
SELECT
amount
FROM payments;
SELECT
_value.amount
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:
SELECT
_key AS deviceId
, _value AS temperature
FROM iot_data
Accessing metadata
Use the _meta
keyword to address the metadata. For example:
SELECT
_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:
SELECT
amount as user.amount
userId as user.id
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:
SELECT
amount as result.amount,
amount + 5 as result.amount
FROM payments;
will generate a structure like the following:
{
"result": {
"amount" : 10,
"amount0": 15
}
}
Nested queries
The tabled query allows you to nest queries. Let us take the query in the previous section and say we are only interested in those entries where there exist more than 1 customer per country.
SELECT *
FROM (
SELECT
COUNT(*) AS count
, country
FROM customer
GROUP BY country
)
WHERE count > 1
Run the query, and you will only see those entries for which there is more than one person registered per country.
Functions
Functions can be used directly.
For example, the ROUND
function allows you to round numeric functions:
SELECT
name
, ROUND(quantity * price) AS rounded_total
FROM groceries
/*The output:
Fairtrade Bananas 2
Meridian Crunchy Peanut Butter 3
Green & Black's organic 85% dark chocolate bar 4
Activia fat free cherry yogurts 6
Green & Blacks Organic Chocolate Ice Cream 8
*/
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