By Rian O’Cuinneagain
JSON can hold more complex structures than CSV files which is useful. However, this can also introduce some added complexity during ingestion.
Datatypes
Data brought from JSON to kdb+ will only ever come as one of:
- String
- Float
- Boolean
This means as well as parsing the data from JSON often we will want to cast to a more suitable datatype.
Take this example converting a long in kdb+ to JSON using .j.j and parsing it back with .j.k
//Roundtrip fails - the input does not equal the output
6~.j.k .j.j 60b//The problem comes from all numerics in JSON being converted to floats
.j.k .j.j 66f
JSON table encoding
//Create a sample table
tab:([] longCol:1 2;
floatCol:4 5f;
symbolCol:`b`h;
stringCol:("bb";"dd");
dateCol:2018.11.23 2018.11.23;
timeCol:00:01:00.000 00:01:00.003)
tab
longCol floatCol symbolCol stringCol dateCol timeCol
------------------------------------------------------------
1 4 b "bb" 2018.11.23 00:01:00.000
2 5 h "dd" 2018.11.23 00:01:00.003meta tabc | t f a
---------| -----
longCol | j
floatCol | f
symbolCol| s
stringCol| C
dateCol | d
timeCol | t//Round trip to JSON results in many differences
.j.k .j.j tab
meta .j.k .j.j tablongCol floatCol symbolCol stringCol dateCol timeCol
----------------------------------------------------------------
1 4 ,"b" "bb" "2018-11-23" "00:01:00.000"
2 5 ,"h" "dd" "2018-11-23" "00:01:00.003"
c | t f a
---------| -----
longCol | f
floatCol | f
symbolCol| C
stringCol| C
dateCol | C
timeCol | C//Use lower case casts on numerics and capital case tok on string type data
//* will leave a column untouched
flip "j*S*DT"$flip .j.k .j.j tab
tab~flip "j*S*DT"$flip .j.k .j.j tablongCol floatCol symbolCol stringCol dateCol timeCol
------------------------------------------------------------
1 4 b "bb" 2018.11.23 00:01:00.000
2 5 h "dd" 2018.11.23 00:01:00.003
1bInstead of using flip and having to specify * to leave a column untouched we can write a helper function.
We can pass it a dictionary with the rules we need to perform
helper:{[t;d] ![t;();0b;key[d]!{($;x;y)}'[value d;key d]]}
castRules:`longCol`symbolCol`dateCol`timeCol!"jSDT"
tab~helper[;castRules] .j.k .j.j tab1bRather than force the use of $ we can make a more general helper which can be based a monodic function per column
generalHelper:{[t;d] ![t;();0b;key[d]!{(x;y)}'[value d;key d]]}
castRules:`longCol`symbolCol`dateCol`timeCol!({neg "j"$ x};{`$upper x};"D"$;"T"$)
generalHelper[;castRules] .j.k .j.j tablongCol floatCol symbolCol stringCol dateCol timeCol
------------------------------------------------------------
-1 4 B "bb" 2018.11.23 00:01:00.000
-2 5 H "dd" 2018.11.23 00:01:00.003Field-based JSON encoding
One common use of JSON is objects (key/value pairs) which parse in kdb+ as dictionaries. These are useful for storing sparse datasets which do not make sense to have each key becoming a new column.
c 25 200
read0 `:sample.json"
{"data":"26cd02c57f9db87b1df9f2e7bb20cc7b","expiry":1527796725,"requestID":["b4a566eb-2529-5cf4-1327-857e3d73653e"]}"
"{"result":"success","message":"success","receipt":[123154,4646646],"requestID":["b4a566eb-2529-5cf4-1327-857e3d73653e"]}"
"{"receipt":[12345678,98751466],"requestID":["b4a566eb-2529-5cf4-1327-857e3d73653e"]}"
"{"data":"26cd02c57f9db87b1df9f2e7bb20cc7b","requestID":["b4a566eb-2529-5cf4-1327-857e3d73653e"]}"
"{"receipt":[12345678,98751466],"requestID":["b4a566eb-2529-5cf4-1327-857e3d73653e"]}"
"{"listSize":2,"list":"lzplogjxokyetaeflilquziatzpjagsginnajfpbkomfancdmhmumxhazblddhcc"}"
"{"requestID":["b4a566eb-2529-5cf4-1327-857e3d73653e"]}"One way to manage these items may be to create a utility that will cast any dictionary using keys to control casting rules.
This allows more complex parsing rules for each field.
//Converts JSON to q with rules per key
decode:{[j]k:.j.k j;(key k)!j2k[key k]@'value k}
//Converts q to JSON with rules per key
encode:{[k].j.j (key k)!k2j[key k]@'value k}
//Rules for JSON to q conversion
j2k:(enlist `)!enlist (::);
j2k[`expiry]:{0D00:00:01*`long$x};
j2k[`result]:`$;
j2k[`receipt]:`long$;
j2k[`id]:{"G"$first x};
j2k[`listSize]:`long$;
j2k[`data]:cut[32];
j2k[`blockCount]:`long$;
j2k[`blocks]:raze;
//Rules for q to JSON conversion
k2j:(enlist `)!enlist (::);
k2j[`expiry]:{`long$%[x;0D00:00:01]};
k2j[`result]:(::);
k2j[`receipt]:(::);
k2j[`id]:enlist;
k2j[`listSize]:(::);
k2j[`data]:raze;
k2j[`blocks]:(::);//Using default .j.k our structures are not transferred as we wish
{show .j.k x} each read0 `:sample.json;data | "26cd02c57f9db87b1df9f2e7bb20cc7b"
expiry | 1.527797e+009
requestID| ,"b4a566eb-2529-5cf4-1327-857e3d73653e"
result | "success"
message | "success"
receipt | 123154 4646646f
requestID| ,"b4a566eb-2529-5cf4-1327-857e3d73653e"
receipt | 1.234568e+007 9.875147e+007
requestID| ,"b4a566eb-2529-5cf4-1327-857e3d73653e"
data | "26cd02c57f9db87b1df9f2e7bb20cc7b"
requestID| ,"b4a566eb-2529-5cf4-1327-857e3d73653e"
receipt | 1.234568e+007 9.875147e+007
requestID| ,"b4a566eb-2529-5cf4-1327-857e3d73653e"
listSize| 2f
list | "lzplogjxokyetaeflilquziatzpjagsginnajfpbkomfancdmhmumxhazblddhcc"
requestID| "b4a566eb-2529-5cf4-1327-857e3d73653e"//Using decode utility captures complex structures
{show decode x} each read0 `:sample.json;data | ,"26cd02c57f9db87b1df9f2e7bb20cc7b"
expiry | 17682D19:58:45.000000000
requestID| ,"b4a566eb-2529-5cf4-1327-857e3d73653e"
result | `success
message | "success"
receipt | 123154 4646646
requestID| ,"b4a566eb-2529-5cf4-1327-857e3d73653e"
receipt | 12345678 98751466
requestID| ,"b4a566eb-2529-5cf4-1327-857e3d73653e"
data | "26cd02c57f9db87b1df9f2e7bb20cc7b"
requestID| "b4a566eb-2529-5cf4-1327-857e3d73653e"
receipt | 12345678 98751466
requestID| ,"b4a566eb-2529-5cf4-1327-857e3d73653e"
listSize| 2
list | "lzplogjxokyetaeflilquziatzpjagsginnajfpbkomfancdmhmumxhazblddhcc"
requestID| "b4a566eb-2529-5cf4-1327-857e3d73653e"//The encode utility allows us to round trip
{sample~{encode decode x} each sample:read0 x}`:sample.json1bQuerying unstructured data
With the release of Anymap in kdb+ 3.6 unstructured data has become much easier to manage in kdb+.
However, some considerations do need to be taken in to account.
sample:([] data:decode each read0 `:sample.json)
sampledata
-----------------------------------------------------------------------------------------------------------------------------
`data`expiry`requestID!(,"26cd02c57f9db87b1df9f2e7bb20cc7b";17682D19:58:45.000000000;,"b4a566eb-2529-5cf4-1327-857e3d73653e")
`result`message`receipt`requestID!(`success;"success";123154 4646646;,"b4a566eb-2529-5cf4-1327-857e3d73653e")
`receipt`requestID!(12345678 98751466;,"b4a566eb-2529-5cf4-1327-857e3d73653e")
`data`requestID!(,"26cd02c57f9db87b1df9f2e7bb20cc7b";,"b4a566eb-2529-5cf4-1327-857e3d73653e")
`receipt`requestID!(12345678 98751466;,"b4a566eb-2529-5cf4-1327-857e3d73653e")
`listSize`list!(2;"lzplogjxokyetaeflilquziatzpjagsginnajfpbkomfancdmhmumxhazblddhcc")
(,`requestID)!,,"b4a566eb-2529-5cf4-1327-857e3d73653e"
Indexing at depth allows the sparse data within the dictionaries to be queried easily
select data[;`requestID] from samplex
---------------------------------------
,"b4a566eb-2529-5cf4-1327-857e3d73653e"
,"b4a566eb-2529-5cf4-1327-857e3d73653e"
,"b4a566eb-2529-5cf4-1327-857e3d73653e"
,"b4a566eb-2529-5cf4-1327-857e3d73653e"
,"b4a566eb-2529-5cf4-1327-857e3d73653e"
0N
,"b4a566eb-2529-5cf4-1327-857e3d73653e"When a key is missing from a dictionary kdb+ will return a null value.
The type of this null is determined by the type of the first key within the dictionary.
This poses an issue.
//Many different nulls are returned
select data[;`expiry] from samplex
------------------------
17682D19:58:45.000000000
`
`long$()
,""
`long$()
0N
,""//Succeds on first 2 rows as by chance only null returned in a atom null
select from (2#sample) where null data[;`expiry]
//Fails once moving to 3 rows as there is an empty list null
select from (3#sample) where null data[;`expiry]data
-------------------------------------------------------------------------------------------------------------
`result`message`receipt`requestID!(`success;"success";123154 4646646;,"b4a566eb-2529-5cf4-1327-857e3d73653e")
evaluation error:
type
[0] select from (3#sample) where null data[;`expiry]
^Checking if a given key is in the dictionary will only return rows which do not have the key
select from sample where `expiry in/:key each data, not null data[;`expiry]data
-----------------------------------------------------------------------------------------------------------------------------
`data`expiry`requestID!(,"26cd02c57f9db87b1df9f2e7bb20cc7b";17682D19:58:45.000000000;,"b4a566eb-2529-5cf4-1327-857e3d73653e")If we prepend each dictionary with the null symbol key “and generic null value(::)` we now can query in a more free manner.
update data:(enlist[`]!enlist (::))(,)/:data from `sample;
sample
data
---------------------------------------------------------------------------------------------------------------------------------
``data`expiry`requestID!(::;,"26cd02c57f9db87b1df9f2e7bb20cc7b";17682D19:58:45.000000000;,"b4a566eb-2529-5cf4-1327-857e3d73653e")
``result`message`receipt`requestID!(::;`success;"success";123154 4646646;,"b4a566eb-2529-5cf4-1327-857e3d73653e")
``receipt`requestID!(::;12345678 98751466;,"b4a566eb-2529-5cf4-1327-857e3d73653e")
``data`requestID!(::;,"26cd02c57f9db87b1df9f2e7bb20cc7b";,"b4a566eb-2529-5cf4-1327-857e3d73653e")
``receipt`requestID!(::;12345678 98751466;,"b4a566eb-2529-5cf4-1327-857e3d73653e")
``listSize`list!(::;2;"lzplogjxokyetaeflilquziatzpjagsginnajfpbkomfancdmhmumxhazblddhcc")
``requestID!(::;,"b4a566eb-2529-5cf4-1327-857e3d73653e")All nulls when a given key is missing are now (::)
select expiry:data[;`expiry] from sampleexpiry
------------------------
17682D19:58:45.000000000
::
::
::
::
::
::The previously failing query can now execute as there are no list type nulls
select from sample where not null data[;`expiry]data
---------------------------------------------------------------------------------------------------------------------------------
``data`expiry`requestID!(::;,"26cd02c57f9db87b1df9f2e7bb20cc7b";17682D19:58:45.000000000;,"b4a566eb-2529-5cf4-1327-857e3d73653e")These (::) can also be replaced with chosen values easily.
Here an infinite value is chosen:
fill:{@[y;where null y;:;x]}
select expiry:fill[0Wn]data[;`expiry] from sampleexpiry
------------------------
17682D19:58:45.000000000
0W
0W
0W
0W
0W
0W
