Aggregations are behaving weirdly with graph display

[BVVLD]

Describe the bug Behavior of the graphical paragraph results becomes inconsistent, when aggregations are applied:

• when user has aggregation + "groupby" with just two fields in the table, user can easily switch between graphs and see the values updating as the query progresses. However, when user has more than 2 fields in the table, and try changing the graph settings while the query is still running, it does not update the server and reflect on the graph, they can only be changed when the query is done.
• The aggregation without groupby does not update the results constantly as it goes, it just shows one value then the paragraph status turns to FINISHED
• In pyspark strange behaviour was noticed, sometimes it displays 'No data available in table' shortly when the query is still running
• Sometimes, when running pyspark, the results appear not in table like it should and are displayed as text, everything combined

Expected behavior The data should display consistently, with graphs updating during paragraph running, if server actually updates the results. Results should not be displayed as text, when table is expected.

How to reproduce Create a notebook with several paragraph: paragraph with query including aggregation + groupby with just two fields in the table, paragraph with query including aggregation + groupby with more than two fields in the table, paragraph with pyspark query.

Software version AJS_7.0.0

Additional context Some problems might originate on the server side, including possible problems with configurations saving and applying during the paragraph run. In this case, the issue should be mirrored on zep_01 project and some changes should be applied to UI to prevent these scenarios before problem is fixed on the server.

[BVVLD]

the problem with pyspark seems to originate from server sending paragraph updates with this: { "code": "SUCCESS", "msg": [ { "type": "TABLE", "data": "count(_time)\n0\n" }, { "type": "TEXT", "data": "\n{'id': '72431276-0e55-4570-b4ba-44e50cf5cce9', 'runId': 'a255e464-2252-4113-9780-62b48aebd400', 'name': 'PySparkExample', 'timestamp': '2024-04-26T06:24:20.717Z', 'batchId': 1, 'numInputRows': 0, 'inputRowsPerSecond': 0.0, 'processedRowsPerSecond': 0.0, 'durationMs': {'getEndOffset': 0, 'setOffsetRange': 9, 'triggerExecution': 10}, 'stateOperators': [{'numRowsTotal': 1, 'numRowsUpdated': 0, 'memoryUsedBytes': 446, 'customMetrics': {'loadedMapCacheHitCount': 0, 'loadedMapCacheMissCount': 0, 'stateOnCurrentVersionSizeBytes': 302}}], 'sources': [{'description': 'source@5be392a3', 'startOffset': {'version': 1, 'archiveOffset': {'offset': -9223372036854775808, 'json': '-9223372036854775808'}}, 'endOffset': {'version': 1, 'archiveOffset': {'offset': -9223372036854775808, 'json': '-9223372036854775808'}}, 'numInputRows': 0, 'inputRowsPerSecond': 0.0, 'processedRowsPerSecond': 0.0}], 'sink': {'description': 'MemorySink'}}\n" } ] } i.e. two separate results where one is an incomplete table, and another is a text with stringified data