I am trying to convert the following druid sql query to a druid json query, as one of the columns i have is a multi-value dimension for which druid does not support a sql style query.
My sql query:
SELECT date_dt, source, type_labels, COUNT(DISTINCT unique_p_hll)
FROM "test"
WHERE
type_labels = 'z' AND
(a_id IN ('a', 'b', 'c') OR b_id IN ('m', 'n', 'p'))
GROUP BY date_dt, source, type_labels;
unique_p_hll is an hll column with uniques.
The druid json query i came up with is following:
{
"queryType": "groupBy",
"dataSource": "test",
"granularity": "day",
"dimensions": ["source", "type_labels"],
"limitSpec": {},
"filter": {
"type": "and",
"fields": [
{ "type": "selector", "dimension": "type_labels", "value": "z" },
{ "type": "or", "fields": [
{ "type": "in", "dimension": "a_id", "values": ["a", "b", "c"] },
{ "type": "in", "dimension": "b_id", "values": ["m", "n", "p"] }
]}
]
},
"aggregations": [
{ "type": "longSum", "name": "unique_p_hll", "fieldName": "p_id" }
],
"intervals": [ "2018-08-01/2018-08-02" ]
}
But the json query seems to be returning empty resultset.
I can see the output correctly in Pivot UI. Though the array column type_labels values show up as {"array_element": "z"} instead of simply "z".
Does the query return empty string, or does it return a formatted JSON with zero records?
If the former, I can suggest a couple of leads for debugging this issue:
Make sure that the query is properly sent to the Broker, as shown in Druid's query tutorial:
curl -X 'POST' -H 'Content-Type:application/json' -d #query-file.json http://<BROKER-IP>:<BROKER-PORT>/druid/v2?pretty
Also, check the Broker's log for errors.
Related
Starting out with JSONB data type and I'm hoping someone can help me out.
I have a table (properties) with two columns (id as primary key and data as jsonb).
The data structure is:
{
"ProductType": "ABC",
"ProductName": "XYZ",
"attributes": [
{
"name": "Color",
"type": "STRING",
"value": "Silver"
},
{
"name": "Case",
"type": "STRING",
"value": "Shells"
},
...
]
}
I would like to update the value of a specific attributes element by name for a row with a given id. For example, for the element with "name"="Case" change the value to "Glass". So it ends up like
{
"ProductType": "ABC",
"ProductName": "XYZ",
"attributes": [
{
"name": "Color",
"type": "STRING",
"value": "Silver"
},
{
"name": "Case",
"type": "STRING",
"value": "Glass"
},
...
]
}
Is this possible with this structure using SQL?
I have created table structure if any of you would like to give it a shot.
dbfiddle
Use the jsonb concatenation operator, ||, to replace keys on the fly:
WITH properties (id, data) AS (
values
(1, '{"ProductType": "ABC","ProductName": "XYZ","attributes": [{"name": "Color","type": "STRING","value": "Silver"},{"name": "Case","type": "STRING","value": "Shells"}]}'::jsonb),
(2, '{"ProductType": "ABC","ProductName": "XYZ","attributes": [{"name": "Color","type": "STRING","value": "Red"},{"name": "Case","type": "STRING","value": "Shells"}]}'::jsonb)
)
SELECT id,
data||
jsonb_build_object(
'attributes',
jsonb_agg(
case
when attribs->>'name' = 'Case' then attribs||'{"value": "Glass"}'::jsonb
else attribs
end
)
) as data
FROM properties m
CROSS JOIN LATERAL JSONB_ARRAY_ELEMENTS(data->'attributes') as a(attribs)
GROUP BY id, data
Updated fiddle
I recently started experimenting with druid. I have a use case which I'm not able to solve. I have 3 date columns primary_date, date_1 and date_2, amount and client.
I wanted to calulate sum(amount) when date_1 > date_2 when granularity is month. I wanted to calculate this for each month in 6 month interval for each client.
I also wanted to calcutate sum(amount) when date_1 > max(bucket date) for each bucket for 6 months for each client.
{
"queryType" : "groupBy",
"dataSource" : "data_source_xxx",
"granularity" : "month",
"dimensions" : ["client"],
"intervals": ["2019-01-01/2019-07-01"],
"aggregations":[{"type": "doubleSum", "name": "total_amount", "fieldName": "amount"}],
"filter" : {
"type": "select",
"dimension": "client",
"value": "client"
}
}
I wanted to modify the above query to have additional filters I have mentioned.
Any help is highly appreciated.
Thanks
I think you can realize this by using a virtual column, which does the date comparison. Then you should be able to use the virtual column in a filtered aggregation, which only applies the aggregation if the filter matches.
This is not tested, but I think something like this should work:
{
"queryType": "groupBy",
"dataSource": "data_source_xxx",
"intervals": [
"2019-01-01T00:00:00.000Z/2019-07-01T00:00:00.000Z"
],
"dimensions": [
{
"type": "default",
"dimension": "client",
"outputType": "string",
"outputName": "client"
}
],
"granularity": "month",
"aggregations": [
{
"type": "filtered",
"filter": {
"type": "selector",
"dimension": "isOlder",
"value": "1"
},
"aggregator": {
"type": "doubleSum",
"name": "sumAmount",
"fieldName": "amount"
}
}
],
"virtualColumns": [
{
"type": "expression",
"name": "isOlder",
"expression": "if( date_1 > date_2, '1', '0')",
"outputType": "string"
}
],
"context": {
"groupByStrategy": "v2"
}
}
I have created this using this PHP code using this package: https://github.com/level23/druid-client
$client = new DruidClient(['router_url' => 'http://127.0.0.1:8888']);
// Build a select query
$builder = $client->query('data_source_xxx', Granularity::MONTH)
->interval("2019-01-01/2019-07-01")
->select(['client'])
->virtualColumn("if( date_1 > date_2, '1', '0')", 'isOlder')
->sum('amount', 'sumAmount', DataType::DOUBLE, function(FilterBuilder $filterBuilder){
$filterBuilder->where('isOlder', '=', '1');
});
echo $builder->toJson();
I have a question regarding an Apache Druid incubating query.
I have a simple group by to select the number of calls per operator. See here my query:
{
"queryType": "groupBy",
"dataSource": "ivr-calls",
"intervals": [
"2019-12-06T00:00:00.000Z/2019-12-07T00:00:00.000Z"
],
"dimensions": [
{
"type": "lookup",
"dimension": "operator_id",
"outputName": "value",
"name": "ivr_operator",
"replaceMissingValueWith": "Unknown"
},
{
"type": "default",
"dimension": "operator_id",
"outputType": "long",
"outputName": "id"
}
],
"granularity": "all",
"aggregations": [
{
"type": "longSum",
"name": "calls",
"fieldName": "calls"
}
],
"limitSpec": {
"type": "default",
"limit": 999999,
"columns": [
{
"dimension": "value",
"direction": "ascending",
"dimensionOrder": "numeric"
}
]
}
}
In this query I order the result by the "value" dimension, I receive 218 results.
I noticed that some of the records are duplicate. (I see some operators two times in my resultset). This is strange because in my experience all dimensions which you select are also used for grouping by. So, they should be unique.
If I add an order by to the "id" dimension, I receive 183 results (which is expected):
"columns": [
{
"dimension": "value",
"direction": "ascending",
"dimensionOrder": "numeric"
},
{
"dimension": "id",
"direction": "ascending",
"dimensionOrder": "numeric"
}
]
The documentation tells me nothing about this strange behavior (https://druid.apache.org/docs/latest/querying/limitspec.html).
My previous experience with druid is that the order by is just "ordering".
I am running druid version 0.15.0-incubating-iap9.
Can anybody tell me why there is a difference in the result set based on the column sorting?
I resolved this problem for now by specifying all columns in my order by.
Issue seems to be related to a bug in druid. See: https://github.com/apache/incubator-druid/issues/9000
I have event data from Kafka with the following structure that I want to ingest in Druid
{
"event": "some_event",
"id": "1",
"parameters": {
"campaigns": "campaign1, campaign2",
"other_stuff": "important_info"
}
}
Specifically, I want to transform the dimension "campaigns" from a comma-separated string into an array / multi-valued dimension so that it can be nicely filtered and grouped by.
My ingestion so far looks as follows
{
"type": "kafka",
"dataSchema": {
"dataSource": "event-data",
"parser": {
"type": "string",
"parseSpec": {
"format": "json",
"timestampSpec": {
"column": "timestamp",
"format": "posix"
},
"flattenSpec": {
"fields": [
{
"type": "root",
"name": "parameters"
},
{
"type": "jq",
"name": "campaigns",
"expr": ".parameters.campaigns"
}
]
}
},
"dimensionSpec": {
"dimensions": [
"event",
"id",
"campaigns"
]
}
},
"metricsSpec": [
{
"type": "count",
"name": "count"
}
],
"granularitySpec": {
"type": "uniform",
...
}
},
"tuningConfig": {
"type": "kafka",
...
},
"ioConfig": {
"topic": "production-tracking",
...
}
}
Which however leads to campaigns being ingested as a string.
I could neither find a way to generate an array out of it with a jq expression in flattenSpec nor did I find something like a string split expression that may be used as a transformSpec.
Any suggestions?
Try setting useFieldDiscover: false in your ingestion spec. when this flag is set to true (which is default case) then it interprets all fields with singular values (not a map or list) and flat lists (lists of singular values) at the root level as columns.
Here is a good example and reference link to use flatten spec:
https://druid.apache.org/docs/latest/ingestion/flatten-json.html
Looks like since Druid 0.17.0, Druid expressions support typed constructors for creating arrays, so using expression string_to_array should do the trick!
I have a series of deeply nested json strings in a pyspark dataframe column. I need to explode and filter based on the contents of these strings and would like to add them as columns. I've tried defining the StructTypes but each time it continues to return an empty DF.
Tried using json_tuples to parse but there are no common keys to rejoin the dataframes and the row numbers dont match up? I think it might have to do with some null fields
The sub field can be nullable
Sample JSON
{
"TIME": "datatime",
"SID": "yjhrtr",
"ID": {
"Source": "Person",
"AuthIFO": {
"Prov": "Abc",
"IOI": "123",
"DETAILS": {
"Id": "12345",
"SId": "ABCDE"
}
}
},
"Content": {
"User1": "AB878A",
"UserInfo": "False",
"D": "ghgf64G",
"T": "yjuyjtyfrZ6",
"Tname": "WE ARE THE WORLD",
"ST": null,
"TID": "BPV 1431: 1",
"src": "test",
"OT": "test2",
"OA": "test3",
"OP": "test34
},
"Test": false
}