Influxdb crashing on large query

InfluxDB 2
1

Hey,
I have a lot of financial data in minute resulution, and I want to resample to daily and save it in another bucket for quick access. I have a query doing just that, which works great if I run it with filters that caps the amount of data it uses, such as only on a few “tickers” (stock names, that are a tag), or only for a small range of time.
But when I let it go on all the data, the memory consumption maxes out and influx finally crashes.

What would be best practices to get around this? Do I need to make a scrip that runs the query on smaller chunks of date ranges, or can I make my query better?

Here’s my query:

vars = {start: 2022-08-01T00:00:00Z, stop: 2022-08-15T00:00:00Z}

DATA =
    from(bucket: "stock_data_collector_v2")
        |> range(start: vars.start, stop: vars.stop)
        |> filter(
            fn: (r) => r._field == "o" or r._field == "h" or r._field == "l" or r._field == "c" or r._field == "v",
        )

EndTime =
    DATA
        |> filter(fn: (r) => r._field == "c")
        |> window(every: 1d)
        |> max(column: "_time")
        |> keep(columns: ["_start", "_time", "_measurement", "ticker"])

o =
    DATA
        |> filter(fn: (r) => r._field == "o")
        |> window(every: 1d)
        |> first()
        |> keep(columns: ["_field", "_value", "_start", "ticker"])

h =
    DATA
        |> filter(fn: (r) => r._field == "h")
        |> window(every: 1d)
        |> max()
        |> keep(columns: ["_field", "_value", "_start", "ticker"])

l =
    DATA
        |> filter(fn: (r) => r._field == "l")
        |> window(every: 1d)
        |> min()
        |> keep(columns: ["_field", "_value", "_start", "ticker"])

c =
    DATA
        |> filter(fn: (r) => r._field == "c")
        |> window(every: 1d)
        |> last()
        |> keep(columns: ["_field", "_value", "_start", "ticker"])

vol =
    DATA
        |> filter(fn: (r) => r._field == "v")
        |> window(every: 1d)
        |> sum()
        |> keep(columns: ["_field", "_value", "_start", "ticker"])

j1 =
    join(tables: {EndTime, o}, on: ["_start", "ticker"])
        |> drop(columns: ["_start"])
        |> group(columns: ["ticker"])
        |> to(bucket: "resample_test", org: "Tlng")

j2 =
    join(tables: {EndTime, h}, on: ["_start", "ticker"])
        |> drop(columns: ["_start"])
        |> group(columns: ["ticker"])
        |> to(bucket: "resample_test", org: "Tlng")

j3 =
    join(tables: {EndTime, l}, on: ["_start", "ticker"])
        |> drop(columns: ["_start"])
        |> group(columns: ["ticker"])
        |> to(bucket: "resample_test", org: "Tlng")

j4 =
    join(tables: {EndTime, c}, on: ["_start", "ticker"])
        |> drop(columns: ["_start"])
        |> group(columns: ["ticker"])
        |> to(bucket: "resample_test", org: "Tlng")

j5 =
    join(tables: {EndTime, vol}, on: ["_start", "ticker"])
        |> drop(columns: ["_start"])
        |> group(columns: ["ticker"])
        |> to(bucket: "resample_test", org: "Tlng")

Thanks!