Implement simple InfluxDB client (#19)

* Implement simple InfluxDB client

* Put InfluxDBClient inside a module, rename it to be consistent with Influx API

* Rename influx.jl to influxdb.jl

---------

Co-authored-by: Suvayu Ali <[email protected]>

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

@@ -2,6 +2,8 @@ module TulipaIO
 
 include("exceptions.jl")
 
+# InfluxDB client
+include("influx.jl")
 # ESDL JSON parser
 include("parsers.jl")
 

src/influxdb.jl

@@ -0,0 +1,49 @@
+module InfluxDB
+
+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 InfluxDBClient
+    host::String
+    database::String
+    port::Int
+    path::String
+    username::String
+    password::String
+end
+
+InfluxDBClient(host::String, database::String) =
+    InfluxDBClient(host, database, 8086, "query", "", "")
+
+function query(
+    client::InfluxDBClient,
+    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
+
+end