Implement simple InfluxDB client

Project.toml

@@ -6,7 +6,9 @@ version = "0.1.0"
 [deps]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 DuckDB = "d2f5444f-75bc-4fdf-ac35-56f514c445e1"
+HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3"
 JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
 PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"

src/TulipaIO.jl

@@ -1,5 +1,6 @@
 module TulipaIO
 
+include("influx.jl")
 include("parsers.jl")
 include("pipeline.jl")
 

src/influx.jl

@@ -0,0 +1,50 @@
+import JSON3
+import HTTP
+import DataFrames as DF
+import Dates: DateTime
+
+# NOTE: this doesn't actually do anything smart like batching
+# or keeping an open connection, it just remembers the connection
+# details
+struct InfluxClient
+    host::String
+    database::String
+    port::Int
+    path::String
+    username::String
+    password::String
+    InfluxClient(
+        host::String,
+        database::String,
+        port::Int = 8086,
+        path::String = "query",
+        username::String = "",
+        password::String = "",
+    ) = new(host, database, port, username, password, path)
+end
+
+function query(
+    client::InfluxClient,
+    measurement::String,
+    time_range_start::DateTime,
+    time_range_end::DateTime,
+)
+    # NOTE: the query is not escaped, so no untrusted input should be accepted here
+    db_query = "SELECT time, value FROM \"$measurement\" WHERE time >= $time_range_start AND time <= $time_range_end"
+    url_params = ["db" => client.database, "q" => db_query]
+    uri = HTTP.URI(;
+        scheme = "http",
+        host = client.host,
+        path = client.path,
+        port = client.port,
+        query = url_params,
+    )
+
+    response = HTTP.get(uri)
+    parsed = JSON3.read(response.body)
+
+    rows = parsed["results"][1]["series"][1]["values"]
+    columns = [[x[1] for x in rows], [x[2] for x in rows]]
+    df = DF.DataFrame(columns, parsed["results"][1]["series"][1]["columns"])
+    return df
+end