Updates to readme and data generation script

2 files changed, 17 insertions, 4 deletions

diff --git a/README.md b/README.md
index ed1b139..9b456e5 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,14 @@
-# 1trc
\ No newline at end of file
+# One Trillion Row Challenge
+
+Inspired by Gunnar Morling's [one billion row challenge](https://github.com/gunnarmorling/1brc), we thought we'd take things one step further and start the one trillion row challenge (1TRC).
+
+## Data Generation
+
+You can generate the dataset yourself using the [data generation script](generate_data.py). We've also hosted the dataset in a requester pays S3 bucket s3://coiled-datasets-rp/1trc in `us-east-1`. 
+
+It draws a random sample of weather stations and normally distributed temperatures drawn from the mean for each station based on the values in [lookup.csv](lookup.csv).
+## The Challenge
+
+The main task, like the 1BRC, is to calculate the min, mean, and max values per weather station, sorted alphabetically.
+
+
diff --git a/generate_data.py b/generate_data.py
index eca7e2a..fa30785 100644
--- a/generate_data.py
+++ b/generate_data.py
@@ -10,7 +10,7 @@ n = 1_000_000_000_000  # Total number of rows of data to generate
 chunksize = 10_000_000  # Number of rows of data per file
 std = 10.0  # Assume normally distributed temperatures with a standard deviation of 10
 lookup_df = pd.read_csv("lookup.csv")  # Lookup table of stations and their mean temperatures
-bucket = "s3://oss-scratch-space/1trc"
+bucket = "s3://coiled-datasets-rp/1trc"
 
 
 def generate_chunk(partition_idx, bucket, chunksize, std, lookup_df):
@@ -35,10 +35,10 @@ def generate_chunk(partition_idx, bucket, chunksize, std, lookup_df):
     df.station = df.station.map(lookup_df.station)
 
     # Save this chunk to the output file
-    filename = f"measurements-{partition_idx}.txt"
+    filename = f"measurements-{partition_idx}.parquet"
     with tempfile.TemporaryDirectory() as tmpdir:
         local = os.path.join(tmpdir, filename)
-        df.to_csv(local, sep=";", header=False, index=False)
+        df.to_parquet(local, engine="pyarrow")
         fs = fsspec.filesystem("s3")
         fs.put(local, f"{bucket}/{filename}")