Convert whole row into a JSON string in MySQL - mysql

I want to log deleted rows into a single column, by converting them into a JSON string.
How can I convert the whole row into JSON without specifying column names one by one?
For example result I want to convert the result of the following query into a JSON, without specifying each column name:
select * from table_1 limit 1

Related

Store JSON in multiple columns and concatenate them to query them

I have a table that currently has a extended column 32,768 bytes in size and so the database uses that space no matter if we put in 1 byte or all 32,768.
I store json in this column.
I am needing to reduce the size this column is taking.
Can I store the json in multiple columns and then concatenate the columns to work with the complete JSON?
For example
column has data:
'{"REC_TYPE_IND":"1","ID":"999999","2nd ID":"1111","location":"0003","BEGIN_DT":"20000101","END_DT":"20991231"}'
I want to split it out like
column1:
'{"REC_TYPE_IND":"1","ID":"999999","2nd '
column2:
'ID":"1111","location":"0003","BEGIN_DT":"20000101","END_DT":"20991231"}'
The how to I use built in functions like json_value(column1 || column2,'location') to get a value?
The error I get when trying the above is:
ORA-00932: inconsistent datatypes: expected - got CHAR

MySQL Query - Convert ResultSet to JSON

I need to convert select * from {table_name} into JSON output.Instead of specifying separate key-value pair in 'JSON_OBJECT'.
I have tried this query
select JSON_ARRAYAGG(json_object("col_name_1",alias.column_1,"col_name_2",alias.column_2)) from {table_name} alias;
This one is giving me results what I need, but my question is instead of giving as key-value pair in json_object I need to fetch all columns by giving something like '*'.
MySQL Database Version: 5.7.24

Replacing multiple values in a string in MYSQL

I have a column in my table which has below values and I need the o/p in the following format:
(Example: S-S-661679. It should in the format S-######).
The third o/p below is the required o/p in the correct format.
Column Value are like:
'S-S-652235'
'S-xjq113465'
**'S-652235'**
'-641408'
I am using MySQL to query this data from the table. Few columns have the correct value but there are alphabets also and the final o/p should have just S-######. I was thinking of using Regular Expression but could not figure out how to use that in SELECT Clause, all the examples I have seen, I see that it can only be used in WHERE Clause.
I am using below code:
**CONCAT('S-', REPLACE(REPLACE(seminar_id, ',', ''), 's', ''))** in the select clause, but it is replicating the 'S-' which is already present in some columns. I am trying to figure out how to replace multiple 'S-' and also the alphabetical characters from the values in the columns.
(Example: S-S-661679. It should in the format S-######).
The third o/p below is the required o/p in the correct format and the highlighted in the image attached is the correct format.
Column Value are like:
'S-S-652235'
'S-xjq113465'
**'S-652235'**
'-641408'

BigQuery getting exhausted after a few json extraction

I have created a json extraction query from a string field in BigQuery following this answer. But interestingly this query is always working for a first few rows then it is returning empty result.
I checked whether the empty results are because they are malformed, but if I remove the rows of the first few correct output rows from the input table then again I am left with first few proper output rows and the rest of them are empty. It seems like BigQuery is getting exhausted after json extraction from string in first few rows.
#standardSQL
SELECT ObjectID,
ARRAY(
SELECT AS STRUCT
key,
ARRAY_LENGTH(SPLIT(value)) `count`,
ARRAY(SELECT cast(x as int64) FROM UNNEST(SPLIT(SUBSTR(value, 2 , LENGTH(value) - 2))) x) AS positions
FROM UNNEST(REGEXP_EXTRACT_ALL(JSON_EXTRACT(indexed_abstract, '$.InvertedIndex'), r'"[^"]+":\[[\d,]*?]')) pair,
UNNEST([STRUCT(REPLACE(SPLIT(pair, ':')[OFFSET(0)], '"', '') AS key, SPLIT(pair, ':')[OFFSET(1)] AS value)])
) keyword
FROM `project.dataset.objects_100`
The input table project.dataset.objects_100 is created from this newline delimited objects_100.json file.
When I run the above query on the input table in the output I get the correct result for the first 12 rows then for the following rows I get empty result which looks like below.
I am also putting this strange output of the query as newline delimited json file in this results_100.json file.
Can someone help me figure out why the output for later rows are becoming empty?
Edit:
Here are a few rows of json where the query returned empty:
{"objectID":"2876639571","indexed_abstract":"{\"IndexLength\":105,\"InvertedIndex\":{\"PROBLEM\":[0],\"TO\":[1],\"BE\":[2],\"SOLVED:\":[3],\"To\":[4],\"provide\":[5],\"an\":[6,68],\"optical\":[7,31],\"operation\":[8,32],\"processor\":[9],\"with\":[10,46,84],\"which\":[11],\"inputting/\":[12],\"outputting\":[13],\"from\":[14],\"plural\":[15,34],\"functional\":[16,20,35,98],\"elements\":[17,36,99],\"to\":[18],\"one\":[19],\"element\":[21],\"consisting\":[22],\"of\":[23,49,55,58],\"the\":[24,47,56,77,97],\"device\":[25,81],\"is\":[26,82],\"enabled.\":[27],\"SOLUTION:\":[28],\"Concerning\":[29],\"this\":[30,80],\"processor,\":[33],\"(thin\":[37],\"film\":[38],\"elements)\":[39],\"2,\":[40,100],\"4,\":[41,101],\"6\":[42,102],\"and\":[43,52,90,103],\"8\":[44],\"provided\":[45,83],\"monomolecule\":[48],\"organic\":[50],\"compound\":[51],\"nanoparticles\":[53],\"composed\":[54],\"aggregate/agglomerate\":[57],\"these\":[59],\"monomolecules\":[60],\"are\":[61],\"arranged\":[62],\"for\":[63,92],\"processing\":[64],\"information\":[65,74],\"by\":[66],\"operating\":[67],\"external\":[69],\"signal\":[70],\"upon\":[71],\"incidental\":[72],\"two-dimensional\":[73],\"light.\":[75],\"At\":[76],\"same\":[78],\"time,\":[79],\"light\":[85],\"sources\":[86],\"I,\":[87],\"II,\":[88],\"III\":[89],\"IV\":[91],\"transferring\":[93],\"image\":[94],\"signals\":[95],\"among\":[96],\"8.\":[104]}}"}
{"objectID":"2787463273","indexed_abstract":"{\"IndexLength\":131,\"InvertedIndex\":{\"A\":[0],\"permanent-magnetic\":[1],\"stepping\":[2,16,38],\"motor\":[3,17,22,39],\"is\":[4,18,23,121],\"disclosed.\":[5],\"According\":[6],\"to\":[7,118],\"the\":[8,21,52,68,79,101,107,115,119],\"utility\":[9],\"model,\":[10],\"noise\":[11],\"of\":[12,31,67,82,100],\"a\":[13,36,41,49,74,83,97],\"salient-pole\":[14],\"PM-type\":[15,37],\"reduced\":[19],\"when\":[20],\"driven.\":[24],\"In\":[25],\"an\":[26,45,92,111],\"inner\":[27,53,64,102],\"magnet\":[28,54,65,94,103],\"yoke\":[29,95,104],\"(111)\":[30,56,105],\"each\":[32],\"stator\":[33,47],\"for\":[34,62,90],\"forming\":[35],\"with\":[40,78],\"structure\":[42],\"combined\":[43],\"by\":[44],\"A-phase\":[46],\"and\":[48,59,96,110,126],\"B-phase\":[50],\"stator,\":[51],\"yoke\\u0027s\":[55],\"closing\":[57],\"pin\":[58],\"hole\":[60],\"arranged\":[61],\"engaging\":[63],\"yokes\":[66],\"two\":[69,87],\"stators\":[70],\"are\":[71],\"positioned\":[72],\"at\":[73],\"line\":[75,81,117],\"symmetry\":[76,108],\"position\":[77,85,120],\"center\":[80,116],\"middle\":[84],\"between\":[86],\"bumps\":[88],\"(111e)\":[89],\"fitting\":[91],\"outer\":[93],\"central\":[98],\"point\":[99],\"as\":[106],\"axis;\":[109],\"angle\":[112],\"theta\":[113],\"from\":[114],\"less\":[122],\"than\":[123,128],\"90\":[124],\"degrees\":[125],\"greater\":[127],\"30\":[129],\"degrees.\":[130]}}"}
{"objectID":"2140096340","indexed_abstract":"{\"IndexLength\":198,\"InvertedIndex\":{\"The\":[0],\"perceptual\":[1,25,141,183],\"subband\":[2,19],\"image\":[3,179],\"coder\":[4],\"(PIC)\":[5],\"introduced\":[6],\"by\":[7],\"Safranek\":[8],\"and\":[9,102,189],\"Johnston\":[10],\"(1989),\":[11],\"selects\":[12],\"a\":[13],\"noise\":[14,29],\"target\":[15,30],\"level\":[16,38],\"for\":[17,43,136,177],\"each\":[18,83,137],\"based\":[20,96],\"on\":[21,62,97,165],\"an\":[22,73,87],\"empirically\":[23],\"derived\":[24],\"masking\":[26,91],\"measure.\":[27,92],\"These\":[28],\"levels\":[31],\"are\":[32,155],\"used\":[33],\"to\":[34,57,86,111,130,150,161],\"set\":[35],\"the\":[36,40,63,66,78,90,98,105,112,114,128,132,143,157,166,169,182,194],\"quantization\":[37],\"in\":[39,156,193],\"DPCM\":[41],\"quantizer\":[42,79,133],\"every\":[44],\"particular\":[45],\"subband.\":[46,138],\"It\":[47],\"achieves\":[48,146],\"high\":[49],\"quality\":[50],\"output\":[51],\"at\":[52,82],\"bit\":[53],\"rates\":[54],\"from\":[55],\"0.1\":[56],\"0.9\":[58],\"bits/pixel\":[59],\"(bpp)\":[60],\"depending\":[61,164],\"complexity\":[64],\"of\":[65,89,104,159,168],\"image.\":[67,170],\"In\":[68,124],\"this\":[69],\"paper,\":[70],\"we\":[71],\"present\":[72],\"algorithm\":[74,172],\"that\":[75],\"locally\":[76],\"adapts\":[77],\"step\":[80,134],\"size\":[81,135],\"pixel\":[84],\"according\":[85],\"estimate\":[88,94],\"This\":[93],\"is\":[95,185,190],\"already\":[99],\"coded\":[100,108],\"pixels\":[101],\"predictions\":[103],\"not\":[106,118,191],\"yet\":[107],\"pixels.\":[109],\"Compared\":[110],\"PIC,\":[113],\"proposed\":[115,144],\"method\":[116,145],\"does\":[117],\"require\":[119],\"any\":[120],\"additional\":[121],\"side\":[122],\"information.\":[123],\"fact,\":[125],\"it\":[126],\"eliminates\":[127],\"need\":[129],\"transmit\":[131],\"For\":[139],\"comparable\":[140],\"quality,\":[142],\"compression\":[147,180],\"gains\":[148],\"up\":[149],\"40\":[151],\"percent.\":[152],\"Typical\":[153],\"values\":[154],\"order\":[158],\"20\":[160],\"30\":[162],\"percent,\":[163],\"nature\":[167],\"Our\":[171],\"has\":[173],\"also\":[174],\"better\":[175],\"performance\":[176],\"supra-threshold\":[178],\"since\":[181],\"error\":[184],\"distributed\":[186],\"more\":[187],\"evenly\":[188],\"concentrated\":[192],\"most\":[195],\"sensitive\":[196],\"regions.\":[197]}}"}
{"objectID":"44353701","indexed_abstract":"{\"IndexLength\":232,\"InvertedIndex\":{\"Severe\":[0],\"coronary\":[1,13,83],\"artery\":[2,84],\"stenosis\":[3],\"with\":[4,43,205],\"endothelial\":[5],\"injury\":[6],\"in\":[7,55,124,187,202,230],\"the\":[8,28,32,79,101,104,112,139,180,200,203],\"canine\":[9],\"model\":[10],\"induces\":[11],\"cyclic\":[12],\"flow\":[14,88,96,129],\"variations\":[15],\"(CFVs),\":[16],\"which\":[17],\"are\":[18],\"partially\":[19],\"due\":[20],\"to\":[21,45,100,195,215],\"spontaneous\":[22],\"platelet\":[23],\"aggregation\":[24],\"and\":[25,39,59,68,103,116,135,172],\"dislodgement\":[26],\"at\":[27],\"stenotic\":[29],\"site.\":[30],\"In\":[31],\"present\":[33],\"study,\":[34],\"we\":[35],\"used\":[36],\"anesthetized\":[37],\"open-chest\":[38],\"unsedated\":[40],\"closed-chest\":[41],\"dogs\":[42,189,204],\"CFVs\":[44,58,61,70,107,115,191,206,211],\"investigate\":[46],\"whether\":[47,60],\"oxidative\":[48],\"metabolic\":[49],\"burst\":[50],\"(hydrogen\":[51],\"peroxide\":[52,122,227],\"generation)\":[53],\"occurred\":[54],\"neutrophils\":[56,125],\"during\":[57,210],\"were\":[62,71],\"attenuated\":[63],\"by\":[64,73,91,110,128],\"superoxide\":[65],\"dismutase\":[66],\"(SOD)\":[67],\"catalase.\":[69],\"produced\":[72],\"placing\":[74],\"a\":[75,94,168],\"cylindrical\":[76],\"constrictor\":[77],\"on\":[78],\"left\":[80],\"anterior\":[81],\"descending\":[82],\"(LAD).\":[85],\"LAD\":[86,118],\"blood\":[87,119],\"was\":[89,108,126,136,207],\"monitored\":[90],\"means\":[92],\"of\":[93,106,114,143,151,170],\"Doppler\":[95],\"probe\":[97],\"placed\":[98],\"proximally\":[99],\"constrictor,\":[102],\"severity\":[105],\"expressed\":[109,137],\"both\":[111],\"frequency\":[113],\"mean\":[117,140,181],\"flow.\":[120],\"Hydrogen\":[121],\"generation\":[123],\"measured\":[127],\"cytometry,\":[130],\"using\":[131],\"single\":[132],\"cell\":[133],\"analysis,\":[134],\"as\":[138],\"fluorescence\":[141,182],\"intensity\":[142,183,201],\"2\u2019,\":[144],\"7\u2019-dichlorofluorescein.\":[145],\"Dogs\":[146],\"received\":[147],\"an\":[148],\"intravenous\":[149],\"infusion\":[150],\"saline\":[152],\"(n\":[153],\"=\":[154,160,165,177],\"8),\":[155],\"SOD\":[156,171],\"(5\":[157],\"mg/kg,\":[158],\"n\":[159,164,176],\"7),\":[161,166],\"catalase\":[162,173],\"(5mg/kg,\":[163],\"or\":[167],\"combination\":[169],\"(same\":[174],\"doses,\":[175],\"7).\":[178],\"Although\":[179],\"did\":[184],\"not\":[185],\"change\":[186],\"sham-operated\":[188],\"without\":[190],\"(61.7\":[192],\"\u00b1\":[193,197,213,217],\"15.8\":[194],\"60.4\":[196],\"11.8;\":[198],\"NS),\":[199],\"significantly\":[208],\"increased\":[209],\"(62.2\":[212],\"13.7\":[214],\"79.8\":[216],\"9.8;\":[218],\"P\":[219],\"\\u003c\":[220],\"0.005).\":[221],\"These\":[222],\"results\":[223],\"indicate\":[224],\"that\":[225],\"hydrogen\":[226],\"is\":[228],\"generated\":[229],\"neutrophils.\":[231]}}"}
{"objectID":"2317258756","indexed_abstract":"{\"IndexLength\":227,\"InvertedIndex\":{\"Administration\":[0],\"of\":[1,16,32,57,155,212],\"single\":[2,30,70],\"dose\":[3,31,71],\"nevirapine\":[4],\"(sdNVP)\":[5],\"to\":[6,11,18,68,106,123,187],\"mothers\":[7,127],\"at\":[8,88],\"delivery\":[9],\"and\":[10,78,95,177,194,214,217],\"their\":[12],\"newborns\":[13],\"for\":[14,45],\"prevention\":[15],\"mother\":[17],\"child\":[19],\"HIV\":[20],\"transmission\":[21,25],\"(PMTCT)\":[22],\"significantly\":[23,203],\"decreases\":[24],\"risk.1,2\":[26],\"However\":[27],\"even\":[28],\"a\":[29],\"NVP\":[33,72,162],\"can\":[34,43],\"induce\":[35],\"non-nucleoside\":[36],\"reverse\":[37],\"transcriptase\":[38],\"inhibitor\":[39],\"(NNRTI)\":[40],\"resistance\":[41],\"which\":[42],\"persist\":[44],\"more\":[46,170],\"than\":[47,182],\"six\":[48],\"months.3\":[49],\"Recent\":[50],\"trials\":[51,81],\"therefore\":[52],\"evaluated\":[53],\"whether\":[54,189,219],\"the\":[55,131,172,190],\"effectiveness\":[56],\"NVP-based\":[58,119,147],\"versus\":[59],\"protease\":[60],\"inhibitor-based\":[61],\"antiretroviral\":[62],\"therapy\":[63],\"(ART)\":[64],\"might\":[65],\"differ\":[66],\"according\":[67],\"prior\":[69,99],\"exposure\":[73,100,163],\"in\":[74,126,137,151,159,198],\"both\":[75,103],\"mothers4,\":[76],\"5\":[77],\"children.6,7\":[79],\"Both\":[80],\"included\":[82],\"cohorts\":[83],\"who\":[84,96],\"had\":[85,97,134],\"received\":[86],\"sdNVP\":[87],\"least\":[89],\"6\":[90],\"months\":[91],\"before\":[92],\"enrollment\":[93],\"(PrNVP)\":[94],\"no\":[98,207],\"(no\":[101],\"PrNVP);\":[102],\"randomized\":[104],\"participants\":[105],\"either\":[107],\"NVP-\":[108,193],\"or\":[109],\"lopinavir/ritonavir\":[110],\"(LPV/r)\":[111],\"\u2013\":[112],\"based\":[113],\"ART.\":[114],\"ACTG\":[115],\"5208\":[116],\"demonstrated\":[117],\"that\":[118,146],\"ART\":[120,125,148,196],\"was\":[121,149],\"inferior\":[122,150],\"LPV/r-based\":[124,195],\"with\":[128,200,206],\"PrNVP4,\":[129],\"but\":[130],\"two\":[132],\"regimens\":[133],\"similar\":[135],\"efficacy\":[136],\"previously\":[138],\"unexposed\":[139],\"mothers5.\":[140],\"In\":[141],\"contrast,\":[142],\"IMPAACT\":[143],\"P1060\":[144],\"found\":[145],\"young\":[152],\"children\":[153,160,199],\"regardless\":[154],\"PrNVP6,7.\":[156],\"This\":[157],\"finding\":[158],\"without\":[161],\"has\":[164],\"major\":[165],\"implications\":[166],\"since\":[167],\"LPV/r\":[168],\"is\":[169,179],\"expensive,\":[171],\"liquid\":[173],\"formulation\":[174],\"requires\":[175],\"refrigeration,\":[176],\"it\":[178],\"less\":[180],\"palatable\":[181],\"NVP.8\":[183],\"Our\":[184],\"objectives\":[185],\"were\":[186],\"investigate\":[188],\"differences\":[191,221],\"between\":[192],\"observed\":[197],\"PrNVP\":[201],\"differed\":[202],\"from\":[204],\"those\":[205],\"PrNVP,\":[208],\"identify\":[209],\"other\":[210,225],\"predictors\":[211],\"virologic\":[213],\"clinical\":[215],\"response,\":[216],\"determine\":[218],\"treatment\":[220],\"depended\":[222],\"on\":[223],\"these\":[224],\"predictors.\":[226]}}"}
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Add value from every row in a table and output (Cast JSON string to int)

I'm querying an SQL database that I have read only access to (Cannot edit tables/create columns etc)
My table contains a column with JSON strings that have (Actual strings are much larger, this is just an example) the following syntax
{"value":"442","country":"usa"}
I would like to add the values contained in the JSON string from each row together and output it as readable, if this is possible?
The values are in the same point of the JSON, as shown above. The values vary in length also, most are 3/4 characters long.

Try the following (for MySQL v5.7+):
select sum(json_extract(jsonString, '$.value')) from mytable;
An example of this is here.