A function compare_json_data(source_data_a,source_data_b)
, accepting structures populated with data loaded from json.load()
and comparing for equality.
$ ./compare.py
Compare JSON result is: True
{"exportVersion":8,"dashboards":{"dashboard-1":{"title":"Movement","layout":[{"w":2,"h":2,"x":0,"y":0,"i":"item-1"},{"w":3,"h":2,"x":2,"y":0,"i":"item-2"}],"description":"Shows sensor movement","embedding":{},"filters":[]}},"items":{"item-1":{"title":"","description":"","dashboardId":"dashboard-1","dataSourceId":"data-source-1","iconValue":"gauge","itemType":"chart","filters":[],"missedFields":[],"lookupFields":[],"convertedFields":[],"calculatedFields":[],"channels":{"value":{"field":"acceleration","type":"quantitative","inferredType":"Number","channelType":"aggregation","aggregate":"sum"}},"reductions":{},"customisations":{"options":{"valueLabel":{"enabled":true,"value":null}},"axes":{"value":{"scaleMax":{"enabled":true,"value":"2500"},"scaleMin":{"enabled":true,"value":"1"}}},"channels":{"value":{"numberFormatting":{"enabled":true,"value":"Custom"},"numberDecimals":{"enabled":true,"value":"2"}}},"conditionalFormatting":[]},"chartType":"Gauge","meta":{},"sample":false,"query":null,"queryId":null,"interactiv |
A function compare_json_data(source_data_a,source_data_b)
, accepting structures populated with data loaded from json.load()
and comparing for equality.
$ ./compare.py
Compare JSON result is: True
{"userid": 100, "theamount": 14000, "ending_ts": 1618848883} | |
{"userid": 200, "theamount": 23300, "ending_ts": 1618848883} |
SELECT | |
userid, | |
SUM(amount) AS theamount, | |
tumble_end(timestamp, interval '1' hour) AS ending_ts | |
FROM mystream | |
GROUP BY userid, tumble(timestamp, interval '1' hour) |
{"userid": 100, "amount": 10000, "timestamp": 1618852483} | |
{"userid": 200, "amount": 23000, "timestamp": 1618852483} | |
{"userid": 100, "amount": 4000, "timestamp": 1618848883} | |
{"userid": 200, "amount": 300, "timestamp": 1618848883} |
// hello world | |
function HELLOWORLD() { | |
return "Hello World"; | |
} | |
HELLOWORLD(); |
-- return aggregation of total payments | |
-- over a 1 hour tumbling window | |
SELECT SUM(CAST(amount AS numeric)) AS payment_volume, | |
CAST(TUMBLE_END(eventTimestamp, interval '1' hour) AS varchar) AS ts | |
FROM payments | |
GROUP BY TUMBLE(eventTimestamp, interval '1' hour); |
-- eventTimestamp is the Kafka timestamp | |
-- as unix timestamp. Magically added to every schema. | |
SELECT max(eventTimestamp) FROM solar_inputs; | |
-- make it human readable | |
SELECT CAST(max(eventTimestamp) AS varchar) as TS FROM solar_inputs; | |
-- dete math with interval | |
SELECT * FROM payments | |
WHERE eventTimestamp > CURRENT_TIMESTAMP-interval '10' second; |
-- detect multiple auths in a short window and | |
-- send to lock account topic/microservice | |
SELECT card, | |
MAX(amount) as theamount, | |
TUMBLE_END(eventTimestamp, interval '5' minute) as ts | |
FROM payments | |
WHERE lat IS NOT NULL | |
AND lon IS NOT NULL | |
GROUP BY card, TUMBLE(eventTimestamp, interval '5' minute) | |
HAVING COUNT(*) > 4 -- >4==fraud |
-- new to my airspace | |
SELECT icao, pings FROM ( | |
SELECT icao, speed, | |
COUNT(*) OVER (PARTITION BY icao ORDER BY eventTimestamp RANGE BETWEEN INTERVAL '60' SECOND PRECEDING AND CURRENT ROW) AS pings | |
FROM airplanes | |
) WHERE pings < 5 | |
AND icao like 'A%' |