Postgres and 1000 multiple calls - node.js

Question

I have a DB PostgresSql Server v. 11.7 - which is used 100% for local development only.
Hardware: 16 cores CPU, 112 GB memory, 3TB m.2 SSD (It is running Ubuntu 18.04 - But I get the about the same speed at my windows 10 laptop when I run the exact same query locally on it).
The DB contains ~ 1500 DB table (of the same structure).
Every call to the DB is custom and specific - so nothing to cache here.
From NodeJS I execute a lot of simultaneously calls (via await Promise.all(all 1000 promises)) and afterwards make a lot of different calculations.
Currently my stats look like this (max connection set to the default of 100):
1 call ~ 100ms
1.000 calls ~ 15.000ms (15ms/call)
I have tried to change the different settings of PostgreSQL. For example to change the max connections to 1.000 - but nothing really seems to optimize the performance (and yes - I do remember to restart the PostgreSql service every time I make a change).
How can I make the execution of the 1.000 simultaneously calls as fast as possible? Should I consider to copy all the needed data to another in-memory database like Redis instead?
The DB table looks like this:
CREATE TABLE public.my_table1 (
id int8 NOT NULL GENERATED ALWAYS AS IDENTITY,
tradeid int8 NOT NULL,
matchdate timestamptz NULL,
price float8 NOT NULL,
"size" float8 NOT NULL,
issell bool NOT NULL,
CONSTRAINT my_table1_pkey PRIMARY KEY (id)
);
CREATE INDEX my_table1_matchdate_idx ON public.my_table1 USING btree (matchdate);
CREATE UNIQUE INDEX my_table1_tradeid_idx ON public.my_table1 USING btree (tradeid);
The simple test query - fetch 30 mins of data between two time-stamps:
select * from my_table1 where '2020-01-01 00:00' <= matchdate AND matchdate < '2020-01-01 00:30'
total_size_incl_toast_and_indexes 21 GB total table size --> 143 bytes/row
live_rows_in_text_representation 13 GB total table size --> 89 bytes/row
My NodeJS code looks like this:
const startTime = new Date();
let allDBcalls = [];
let totalRawTrades = 0;
(async () => {
for(let i = 0; i < 1000; i++){
allDBcalls.push(selectQuery.getTradesBetweenDates(tickers, new Date('2020-01-01 00:00'), new Date('2020-01-01 00:30')).then(function (rawTradesPerTicker) {
totalRawTrades += rawTradesPerTicker["data"].length;
}));
}
await Promise.all(allDBcalls);
_wl.info(`Fetched ${totalRawTrades} raw-trades in ${new Date().getTime() - startTime} ms!!`);
})();
I just tried to run EXPLAIN - 4 times on the query:
EXPLAIN (ANALYZE,BUFFERS) SELECT * FROM public.my_table1 where '2020-01-01 00:00' <= matchdate and matchdate < '2020-01-01 00:30';
Index Scan using my_table1_matchdate_idx on my_table1 (cost=0.57..179.09 rows=1852 width=41) (actual time=0.024..0.555 rows=3013 loops=1)
Index Cond: (('2020-01-01 00:00:00+04'::timestamp with time zone <= matchdate) AND (matchdate < '2020-01-01 00:30:00+04'::timestamp with time zone))
Buffers: shared hit=41
Planning Time: 0.096 ms
Execution Time: 0.634 ms
Index Scan using my_table1_matchdate_idx on my_table1 (cost=0.57..179.09 rows=1852 width=41) (actual time=0.018..0.305 rows=3013 loops=1)
Index Cond: (('2020-01-01 00:00:00+04'::timestamp with time zone <= matchdate) AND (matchdate < '2020-01-01 00:30:00+04'::timestamp with time zone))
Buffers: shared hit=41
Planning Time: 0.170 ms
Execution Time: 0.374 ms
Index Scan using my_table1_matchdate_idx on my_table1 (cost=0.57..179.09 rows=1852 width=41) (actual time=0.020..0.351 rows=3013 loops=1)
Index Cond: (('2020-01-01 00:00:00+04'::timestamp with time zone <= matchdate) AND (matchdate < '2020-01-01 00:30:00+04'::timestamp with time zone))
Buffers: shared hit=41
Planning Time: 0.097 ms
Execution Time: 0.428 ms
Index Scan using my_table1_matchdate_idx on my_table1 (cost=0.57..179.09 rows=1852 width=41) (actual time=0.016..0.482 rows=3013 loops=1)
Index Cond: (('2020-01-01 00:00:00+04'::timestamp with time zone <= matchdate) AND (matchdate < '2020-01-01 00:30:00+04'::timestamp with time zone))
Buffers: shared hit=41
Planning Time: 0.077 ms
Execution Time: 0.586 ms