Cloudera SQL Stream Builder (SSB) Released!

CSA 1.3.0 is now available with Apache Flink 1.12 and SQL Stream Builder! Check out this white paper for some details. You can get full details on the Stream Processing and Analytics available from Cloudera here.

This is awesome way to query Kafka topics with continuous SQL that is deployed to scalable Flink nodes in YARN or K8. We can also easily define functions in JavaScript to enhance, enrich and augment our data streams. No Java to write, no heavy deploys or build scripts, we can build, test and deploy these advanced streaming applications all from your secure browser interface.

References:

Example Queries:

SELECT location, max(temp_f) as max_temp_f, avg(temp_f) as avg_temp_f,
min(temp_f) as min_temp_f

FROM weather2

GROUP BY location

SELECT HOP_END(eventTimestamp, INTERVAL '1' SECOND, INTERVAL '30' SECOND) as windowEnd,
count(`close`) as closeCount,

sum(cast(`close` as float)) as closeSum, avg(cast(`close` as float)) as closeAverage,
min(`close`) as closeMin,

max(`close`) as closeMax,

sum(case when `close` > 14 then 1 else 0 end) as stockGreaterThan14

FROM stocksraw
WHERE symbol = 'CLDR'
GROUP BY HOP(eventTimestamp, INTERVAL '1' SECOND, INTERVAL '30' SECOND)

SELECT scada2.uuid, scada2.systemtime, scada2.temperaturef, scada2.pressure, scada2.humidity, scada2.lux, scada2.proximity,

scada2.oxidising,scada2.reducing , scada2.nh3, scada2.gasko,energy2.`current`,

energy2.voltage,energy2.`power`,energy2.`total`,energy2.fanstatus

FROM energy2 JOIN scada2 ON energy2.systemtime = scada2.systemtime

SELECT symbol, uuid, ts, dt, `open`, `close`, high, volume, `low`, `datetime`, 'new-high' message,

'nh' alertcode, CAST(CURRENT_TIMESTAMP AS BIGINT) alerttime

FROM stocksraw st

WHERE symbol is not null

AND symbol <> 'null'

AND trim(symbol) <> '' and

CAST(`close` as DOUBLE) >

(SELECT MAX(CAST(`close` as DOUBLE))
FROM stocksraw s

WHERE s.symbol = st.symbol);

SELECT *

FROM statusevents

WHERE lower(description) like '%fail%'

SELECT

sensor_id as device_id,

HOP_END(sensor_ts, INTERVAL '1' SECOND, INTERVAL '30' SECOND) as windowEnd,

count(*) as sensorCount,

sum(sensor_6) as sensorSum,

avg(cast(sensor_6 as float)) as sensorAverage,

min(sensor_6) as sensorMin,

max(sensor_6) as sensorMax,

sum(case when sensor_6 > 70 then 1 else 0 end) as sensorGreaterThan60

FROM iot_enriched_source

GROUP BY

sensor_id,

HOP(sensor_ts, INTERVAL '1' SECOND, INTERVAL '30' SECOND)

SELECT title, description, pubDate, `point`, `uuid`, `ts`, eventTimestamp

FROM transcomevents

Source Code:

Example SQL Stream Builder Run

We login then build our Kafka data source(s), unless they were predefined.

Next we build a few virtual table sources for Kafka topics we are going to read from. If they are JSON we can let SSB determine the schema for us. Or we can connect to the Cloudera Schema Registry for it to determine the schema for AVRO data.

We can then define virtual table syncs to Kafka or webhooks.

We then run a SQL query with some easy to determine parameters and if we like the results we can create a materialized view.