# Examples

## CEP Examples

CEP Examples – CEP is all about finding interesting events based on some logic, some pattern, which could be beyond just simple point based comparison. Most of the use cases where we need to maintain state for some period of time and look for pattern in continuous manner with every single new event, we will have to do more than simple query and it becomes very hard most of the time to do it on the streaming data.

Complex event processing comes to help us with these kinds of advanced use cases. Whenever it becomes really hard and time consuming then the best option could be machine learning. While with machine learning we may find the pattern with certain confidence and probability, with CEP it’s always absolute and with 100% confidence and probability. However, there are many use cases suitable for ML and many for CEP

This is how cep query (cepq) is defined in the BangDB. You may check it out for quick reference.
Now, before we go and show how to write queries, let’s appreciate the cep use cases and why it’s difficult to do it with “filter” and “join/jqry”.

## Scenario 1

Bank transaction where we have following stream structure;
Basically each event has name of the user, location from where the transaction was initiated, transaction id, amount for the transaction and current balance.

` { "schema" : "myschema", "streams" :[ { "name":"account", "type":1, "swsz":86400, "inpt":[], "attr":[{"name":"name", "type":5, "kysz":32, "sidx":1, "stat":2}, {"name":"loc", "type":5, "kysz":32, "sidx":1, "stat":2}, {"name":"txnid", "type":9, "stat":1}, {"name":"amount", "type":11, "stat":3}, {"name":"balance", "type":11, "stat":3} ] } ] } `

Use case 1

Find all events where amount is more than x anytime, x is a fixed number

x = 10000
` "cepq":[{"name":"amount_exceed_fixed", "type":6, "tloc":86400, "fqry":{"name":"{\"query\":[{\"key\":\"amount\", \"cmp_op\":0, \"val\":10000}]}", "type":1}}] `
cepq.fqry.type = 1 means query is not udf, 2 for udf
and cepq.type = 6 means that simple cep join or cepq where jqry is not present join_type as defined
NOTE: we can do this using filter as well, like this;
` {"name":"myfilter1", "fqry":{"name":"{\"query\":[{\"key\":\"amount\", \"cmp_op\":0, \"val\":10000}]}", "type":1}, "fatr":["name", "loc", "amount", "balance", "txnid"], "ostm":"amount_exceed_balance"} {"name":"myfilter1", "fqry":{"name":"{\"query\":[{\"key\":\"amount\", \"cmp_op\":0, \"val\":\"\$balance\"}],\"qtype\":2}", "type":1}, "fatr":["name", "loc", "amount", "balance", "txnid"], "catr":[{"name":"exceed", "type":11, "opid":15, "iatr":["amount", "balance"]}], "ostm":"amount_exceed_balance"} `
But it will always send the filtered data to some other stream, whereas we won’t have to necessarily send data to other stream in case of cep, it may directly send notification

Use case 2

Find all events where amount is more than x anytime, x is a balance in the current event data

x = the balance in the current event data

` "cepq":[{"name":"amount_exceed_var", "type":6, "tloc":86400, "fqry":{"name":"{\"query\":[{\"key\":\"amount\", \"cmp_op\":0, \"val\":\"\$balance\"}], \"qtype\":2}", "type":1}, "notf":12345}] `
“qtype”: 2 suggest that \$balance is not a number or value in itself, but the field value in the current data

Use case 3

Find all events where amount is more than avg(amount) at any given time

` "cepq":[{"name":"amount_exceed_avg", "type":6, "tloc":86400, "fqry":{"name":"{\"query\":[{\"key\":\"amount\", \"cmp_op\":0, \"val\":\"avg(account.amount, h_1, more_10)\"}],\"qtype\":3}", "type":1}, "notf":12345}] `
“qtype”: 3 means we need to compute the value using the function.
avg(account.amount, h_1, more_10) means we need to take average of amount for 1 hour (h_1 or hour_1) from stream account.
more_10 indicates, 10% more than the average value.

General structure for this function is following;
` func(stream.attribute, GRAN_NUM, MARGIN_NUM) func names are = {"AVG", "SUM", "STD", "MIN", "MAX", "COUNT"}; gran names are = {"MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "YEAR"}; margin names are = {"MORE", "LESS", "ABSMORE", "ABSLESS"}; `
We can write partial or full names for all these, therefore h_1 is HOUR_1, 1 hour.
MORE or LESS is with percentage, i.e. NUM associated is percentage, whereas ABSMORE or ABSLESS is for absolute numbers

Use case 4

Find all events where avg(amount) is more than fix num X

` "cepq":{"name":"amount_exceed_avg_fixed", "type":6, "tloc":86400, "fqry":{"name":"{\"query\":[{\"key\":\"avg(account.amount, h_1, more_10)\", \"cmp_op\":0, \"val\":1010}],\"qtype\":3}", "type":1}, "notf":54321}] `

Use case 5

Find all events where avg(amount) is more than avg(due)

` "cepq":{"name":"amount_exceed_avg_avg", "type":6, "tloc":86400, "fqry":{"name":"{\"query\":[{\"key\":\"avg(account.amount, h_1, more_10)\", \"cmp_op\":0, \"val\":\"avg(account.balance, h_1, more_10)\"}],\"qtype\":3}", "type":1}, "notf":989898}] `

Use case 6

Find all transaction which has same txnid within 100 sec for two different locations

` "cepq":{"name":"txn_fraud_case", "type":1, "tloc":1000, "ratr":["txnid", "loc"], "rstm":"account", "iatr":["name", "balance", "txnid", "loc"], "jqry":{"cond":["txnid", "loc"], "opid":11, "args":["txnid", "loc"], "cmp":["EQ", "NE"]}, "cond":[{"name":"NUMT", "val":1, "opid":1}, {"name":"DUR", "val":100, "opid":0}], "ostm":"txn_fraud", "notf":250975}] `
cepq.type is join_type, 1 means simple join, as defined in details here

Use case 7

Now, let’s go back to IOT case.
Let’s say we wish to find a pattern where n readings of temperature is consistently increasing and higher than a threshold Th…
Let’s define a stream here;

` { "schema" : "myschema", "streams" :[ { "name":"temp_stream", "type":1, "swsz":81600, "inpt":[], "attr":[{"name":"temp", "type":11}, {"name":"point", "type":5, "kysz":32} ], "cepq":[{"name":"temp_inc_pat_join", "type":1, "tloc":3000, "ratr":["temp","point"], "rstm":"temp_stream", "iatr":["temp"], "jqry":{"cond":["point", "temp"], "opid":11, "args":["point", "temp"], "cmp":["EQ", "LT"]}, "fqry":{"name":"{\"query\":[{\"key\":\"temp\", \"cmp_op\":0, \"val\":70.5}]}", "type":1}, "cond":[{"name":"NUMT", "val":3, "opid":1}, {"name":"DUR", "val":1000, "opid":0}], "ostm":"temp_inc_stream", "notf":1}] }, { "name":"temp_inc_stream", "type":3, "inpt":["temp_stream"], "attr":[{"name":"point", "type":5, "kysz":32}, {"name":"temp", "type":11} ] } ] } `
As you see the “cepq” definition here.
cepq.type=1 means join_type is 1 (simple type), it applies fqry to filter events which has temp > 70.5 and then it applies jqry where it checks point is same and then earlier temp is less than this temp and then condition that it happens 3 times within 1000 sec of duration.
Here is the example;
` put [ temp_stream ] : {"temp":70.1, "point":"1"} put [ temp_stream ] : {"temp":71.6, "point":"1"} put [ temp_stream ] : {"temp":72.1, "point":"1"} put [ temp_stream ] : {"temp":72.2, "point":"1"} put [ temp_stream ] : {"temp":72.4, "point":"1"} put [ temp_stream ] : {"temp":72.7, "point":"1"} put [ temp_stream ] : {"temp":73.1, "point":"1"} output is; -[ temp_inc_stream ] event = {"temp":72.40000000000001,"_pk":1585078131443321,"temp":72.2,"point":"1","_jpk1":1585078131431913,"_v":1} -[ temp_inc_stream ] event = {"temp":72.7,"_pk":1585078131454858,"temp":72.40000000000001,"point":"1","_jpk1":1585078131443321,"_v":1} -[ temp_inc_stream ] event = {"temp":73.10000000000001,"_pk":1585078131466322,"temp":72.7,"point":"1","_jpk1":1585078131454858,"_v":1} `
Note: first event is not considered as it couldn’t get filtered due to temp = 70.1 is less than 70.5, rest are all filtered and those are selected for output or notification where temp increased continuously for 3 times within 1000 sec of duration

Use case 8

Join two streams, temp and pressure, such that the pressure is more than 11.3 at least 2 consecutive times within 1000 sec duration for any given point. Let’s look at the stream definition along with “cepq” query.

` { "schema" : "myschema", "streams" :[ { "name":"temp_stream", "type":1, "swsz":81600, "inpt":[], "attr":[{"name":"temp", "type":11}, {"name":"point", "type":9} ], "cepq":[{"name":"temp_pressure_join", "type":3, "tloc":3000, "iatr":["temp", "point"], "rstm":"pressure_stream", "ratr":["pressure"], "jqry":{"cond":["point"], "opid":11, "args":["point"]}, "ostm":"temp_pressure_stream"}] }, { "name":"pressure_stream", "type":1, "inpt":[], "attr":[{"name":"pressure", "type":11}, {"name":"point", "type":9} ], "cepq":[{"name":"temp_pressure_join", "type":5, "tloc":3000, "ratr":["temp", "point"], "rstm":"temp_stream", "iatr":["pressure"], "jqry":{"cond":["point"], "opid":11, "args":["point"]}, "fqry":{"name":"{\"query\":[{\"key\":\"pressure\", \"cmp_op\":0, \"val\":11.3}]}", "type":1}, "cond":[{"name":"NUMT", "val":2, "opid":1}, {"name":"DUR", "val":1000, "opid":0}], "ostm":"temp_pressure_stream", "notf":1}] }, { "name":"temp_pressure_stream", "type":3, "inpt":["temp_stream", "pressure_stream"], "attr":[{"name":"point", "type":9}, {"name":"temp", "type":11}, {"name":"pressure", "type":11} ] } ] } `
Here is how the output may be for given events streaming in;
` put [ temp_stream ] : {"temp":70.1, "point":1} put [ pressure_stream ] : {"pressure":10.2, "point":2} put [ pressure_stream ] : {"pressure":11.1, "point":1} put [ pressure_stream ] : {"pressure":11.5, "point":1} put [ pressure_stream ] : {"pressure":11.8, "point":1} put [ temp_stream ] : {"temp":71.1, "point":2} put [ pressure_stream ] : {"pressure":11.9, "point":1} put [ pressure_stream ] : {"pressure":12.1, "point":2} put [ temp_stream ] : {"temp":71.2, "point":1} put [ pressure_stream ] : {"pressure":12.5, "point":1} put [ pressure_stream ] : {"pressure":12.65, "point":2} put [ pressure_stream ] : {"pressure":12.75, "point":1} The joined stream; -[ temp_pressure_stream ] event = {"pressure":11.8,"_pk":1585079098998843,"temp":70.10000000000001,"point":1,"_jpk1":1585079098950997,"_v":1} -[ temp_pressure_stream ] event = {"pressure":11.9,"_pk":1585079099051050,"temp":70.10000000000001,"point":1,"_jpk1":1585079098950997,"_v":1} -[ temp_pressure_stream ] event = {"pressure":12.65,"_pk":1585079099137108,"temp":71.10000000000001,"point":2,"_jpk1":1585079099029910,"_v":1} -[ temp_pressure_stream ] event = {"pressure":12.75,"_pk":1585079099150755,"temp":71.2,"point":1,"_jpk1":1585079099093652,"_v":1} `
As you see, tuple (11.8, 70.1) is selected as 11.8 exceeds 11.3 (fqry) and two times consecutively increasing (11.5, 11.8) for point 1
same with tuple(11.9, 70.1)
Then since temp (71.2, 1) came in, therefore it counted 2 times again to output (12.75, 71.2) for point 1, meanwhile (12.65, 71.1) also satisfied for point 2

We may also put a point num (ex; 1) in the query to only output for that point; i.e.
` "cepq":[{"name":"temp_pressure_join", "type":5, "tloc":3000, "ratr":["temp", "point"], "rstm":"temp_stream", "iatr":["pressure"], "jqry":{"cond":["point"], "opid":12, "args":["1"]}, "fqry":{"name":"{\"query\":[{\"key\":\"pressure\", \"cmp_op\":0, \"val\":11.3}]}", "type":1}, "cond":[{"name":"NUMT", "val":2, "opid":1}, {"name":"DUR", "val":1000, "opid":0}], "ostm":"temp_pressure_stream", "notf":1}] `

Note: when we changed “point” with “1”, then we also made “opid”:12 (changed from 11)

## Scenario 2

Use case 9

Pizza delivery. Let’s say we wish to get notified or store all the events when status of pizza delivery changes, from received order to delivered and all intermediate statuses.
Let’s look at the first one, where we wish to be notified for all the events where status changed and then if we wish to know only when it was delivered

` { "schema" : "myschema", "streams" :[ { "name":"pizza_stream", "type":1, "swsz":81600, "inpt":[], "attr":[{"name":"oid", "type":9}, {"name":"status", "type":5}, {"name":"custid", "type":9} ], "cepq":[{"name":"pizza_delivery_tracker", "type":1, "tloc":3000, "ratr":["oid", "custid", "status"], "rstm":"pizza_stream", "iatr":["oid", "custid", "status"], "jqry":{"cond":["oid", "custid", "status"], "opid":11, "args":["oid", "custid", "status"], "cmp":["EQ", "EQ", "NE"]}, "cond":[{"name":"NUMT", "val":1, "opid":1}, {"name":"DUR", "val":1000, "opid":0}], "ostm":"pizza_delivery_tracker_stream", "notf":1}] }, { "name":"pizza_delivery_tracker_stream", "type":3, "inpt":["pizza_stream"], "attr":[{"name":"oid", "type":9}, {"name":"status", "type":5}, {"name":"custid", "type":9} ] } ] } `
The pizza_stream has orderid (oid), status and custid. This is joining with self using cepq.type = 1, which is simple join. It runs jqry, which does following join;
prev.oid = cur.oid AND prev.custid = cur.custid AND prev.status != cur.status and when this is satisfied, for 1 time (NUMT = 1) within duration of 1000 sec (DUR = 1000), then it notifies and also sends data to output stream
pizza_delivery_tracker_stream

Here is the output for sample event data;
` put [ pizza_stream ] : {"oid":1, "status":"1", "custid":11} put [ pizza_stream ] : {"oid":2, "status":"1", "custid":12} put [ pizza_stream ] : {"oid":3, "status":"1", "custid":13} put [ pizza_stream ] : {"oid":2, "status":"2", "custid":12} put [ pizza_stream ] : {"oid":2, "status":"2", "custid":12} put [ pizza_stream ] : {"oid":4, "status":"1", "custid":14} put [ pizza_stream ] : {"oid":2, "status":"3", "custid":12} put [ pizza_stream ] : {"oid":3, "status":"2", "custid":13} put [ pizza_stream ] : {"oid":1, "status":"2", "custid":11} put [ pizza_stream ] : {"oid":3, "status":"3", "custid":13} -[ pizza_delivery_tracker_stream ] event = {"oid":2,"custid":12,"status":"2","_pk":1585141967098022,"oid":2,"custid":12,"status":"1","_jpk1":1585141967076688,"_v":1} -[ pizza_delivery_tracker_stream ] event = {"oid":2,"custid":12,"status":"3","_pk":1585141967130940,"oid":2,"custid":12,"status":"2","_jpk1":1585141967098022,"_v":1} -[ pizza_delivery_tracker_stream ] event = {"oid":3,"custid":13,"status":"2","_pk":1585141967142349,"oid":3,"custid":13,"status":"1","_jpk1":1585141967087023,"_v":1} -[ pizza_delivery_tracker_stream ] event = {"oid":1,"custid":11,"status":"2","_pk":1585141967153537,"oid":1,"custid":11,"status":"1","_jpk1":1585141967066211,"_v":1} -[ pizza_delivery_tracker_stream ] event = {"oid":3,"custid":13,"status":"3","_pk":1585141967165227,"oid":3,"custid":13,"status":"2","_jpk1":1585141967142349,"_v":1} `

Use case 10
Let’s get notified only when the pizza is delivered
` { "schema" : "myschema", "streams" :[ { "name":"pizza_stream", "type":1, "swsz":81600, "inpt":[], "attr":[{"name":"oid", "type":9}, {"name":"status", "type":5}, {"name":"custid", "type":9} ], "cepq":[{"name":"pizza_delivery_tracker", "type":1, "tloc":3000, "ratr":["oid", "custid", "status"], "rstm":"pizza_stream", "iatr":["oid", "custid", "status"], "jqry":{"cond":["oid", "custid", "status"], "opid":11, "args":["oid", "custid", "status"], "cmp":["EQ", "EQ", "NE"]}, "cond":[{"name":"NUMT", "val":1, "opid":1}, {"name":"DUR", "val":1000, "opid":0}, {"name":"cqry", "fqry": {"name":"{\"query\":[{\"key\":\"status\", \"cmp_op\":4, \"val\":\"3\"}], \"qtype\":1}", "type":1}}], "ostm":"pizza_delivery_tracker_stream", "notf":1}] }, { "name":"pizza_delivery_tracker_stream", "type":3, "inpt":["pizza_stream"], "attr":[{"name":"oid", "type":9}, {"name":"status", "type":5}, {"name":"custid", "type":9} ] } ] } `
Here we also add fqry (filter) where it only considers if status = 3.
The sample output is;
` put [ pizza_stream ] passed : {"oid":1, "status":"1", "custid":11} put [ pizza_stream ] passed : {"oid":2, "status":"1", "custid":12} put [ pizza_stream ] passed : {"oid":3, "status":"1", "custid":13} put [ pizza_stream ] passed : {"oid":2, "status":"2", "custid":12} put [ pizza_stream ] passed : {"oid":2, "status":"2", "custid":12} put [ pizza_stream ] passed : {"oid":4, "status":"1", "custid":14} put [ pizza_stream ] passed : {"oid":2, "status":"3", "custid":12} put [ pizza_stream ] passed : {"oid":3, "status":"2", "custid":13} put [ pizza_stream ] passed : {"oid":1, "status":"2", "custid":11} put [ pizza_stream ] passed : {"oid":3, "status":"3", "custid":13} -[ pizza_delivery_tracker_stream ] event = {"oid":3,"custid":13,"status":"3","_pk":1585143131803123,"oid":3,"custid":13,"status":"2","_jpk1":1585143131780487,"_v":1} -[ pizza_delivery_tracker_stream ] event = {"oid":2,"custid":12,"status":"3","_pk":1585143131769569,"oid":2,"custid":12,"status":"2","_jpk1":1585143131736568,"_v":1} `