Elasticsearch to SQL equivalent, get the first and last documents with filter - sql

Question

I have an issue with elasticsearch documents. So the goal is I need to get the latest data close from index stocks and also the last data close from stocks within a specified date. Here's the example of the SQL equivalent:
SELECT (
SELECT `close` FROM `stocks` WHERE date >= 2022-06-01 ORDER by date DESC LIMIT 1
) as last_close,
(
SELECT `close` FROM `stocks` ORDER by date ASC LIMIT 1
) as latest_close FROM stocks
That's the goal I need to achieve, for the rest I think I don't need to share because the bottleneck is on that issue.
Edited: This is the mappings on my index stocks in elasticsearch:
{
"stocks": { -
"mappings": { -
"properties": { -
"avg": { -
"type": "double"
},
"board": { -
"type": "text"
},
"book": { -
"type": "double"
},
"change": { -
"type": "double"
},
"chg": { -
"type": "double"
},
"close": { -
"type": "double"
},
"date": { -
"type": "date",
"format": "yyyy-MM-dd HH:mm:ss"
},
"der": { -
"type": "double"
},
"eps": { -
"type": "double"
},
"fve": { -
"type": "double"
},
"fvei": { -
"type": "double"
},
"group": { -
"type": "keyword"
},
"high": { -
"type": "double"
},
"low": { -
"type": "double"
},
"open": { -
"type": "double"
},
"paid_up_cap_shares": { -
"type": "text",
"fields": { -
"keyword": { -
"type": "keyword",
"ignore_above": 256
}
}
},
"pbv": { -
"type": "double"
},
"peg_analysis": { -
"type": "text"
},
"peg_ratio": { -
"type": "double"
},
"per": { -
"type": "double"
},
"prev": { -
"type": "double"
},
"roe": { -
"type": "double"
},
"stock": { -
"type": "keyword"
},
"trade_freq": { -
"type": "double"
},
"trade_val": { -
"type": "double"
},
"trade_vol": { -
"type": "double"
}
}
}
}
}
And here's the document example for the index:
{
"_index": "stocks",
"_id": "6odITIEBRQt2Zq4UUGu3",
"_score": 1.0,
"_source": {
"date": "2022-06-10 13:23:36",
"fvei": "112833.74",
"pbv": "0.0",
"prev": "97",
"book": "-1.185716459E8",
"roe": "-7.87",
"der": "-6.87",
"high": "91",
"avg": "91.0",
"fve": "91.0",
"low": "91",
"stock": "WINR",
"per": "0.0",
"close": "91",
"trade_vol": "46283600",
"group": "IDXPROPERT",
"paid_up_cap_shares": "0.1",
"trade_val": "4211807600",
"chg": "-6.59",
"change": "-6.0",
"peg_ratio": "0.0",
"eps": "9327072.3",
"trade_freq": "1433",
"peg_analysis": "negative growth",
"board": "RG",
"open": "91"
}
},
{
"_index": "stocks",
"_id": "7IdITIEBRQt2Zq4UUGu3",
"_score": 1.0,
"_source": {
"date": "2022-06-10 13:23:36",
"fvei": "66215.12",
"pbv": "0.0",
"prev": "685",
"book": "1946574.58",
"roe": "22.08",
"der": "3.62",
"high": "685",
"avg": "677.5",
"fve": "680.0",
"low": "670",
"stock": "TLDN",
"per": "0.0",
"close": "680",
"trade_vol": "577600",
"group": "IDXNONCYC",
"paid_up_cap_shares": "540.65",
"trade_val": "393001500",
"chg": "-0.73",
"change": "-5.0",
"peg_ratio": "0.0",
"eps": "429847.27",
"trade_freq": "117",
"peg_analysis": "negative growth",
"board": "RG",
"open": "685"
}
},

Answer 1

I have found the answer by using top_hits:
{
"query": {
"bool": {
"must": [
{
// range filter
"range": {
"date": {
"gte": "now-89d",
"lte": "now+1d"
}
}
}
]
}
},
"aggs": {
"group": {
"terms": {
"field": "stock"
},
"aggs": {
"last_doc": {
"top_hits": {
"size": 1,
"sort": [
{
"date": {
"order": "desc"
}
}
],
"_source": {
"includes": ["close"]
}
}
},
"first_doc": {
"top_hits": {
"size": 1,
"sort": [
{
"date": {
"order": "asc"
}
}
],
"_source": {
"includes": ["close"]
}
}
}
}
}
}
}