Add preliminary support for DimensionalCount

This type allows for taking the additional columns of a query and
turning them into dimensions on the cloudwatch metric

lambdas/query-metrics/Cargo.toml

@@ -3,16 +3,13 @@ name = "query-metrics"
 version = "0.1.0"
 edition = "2021"
 
-# Starting in Rust 1.62 you can use `cargo add` to add dependencies 
-# to your project.
-#
-# If you're using an older Rust version,
-# download cargo-edit(https://github.com/killercup/cargo-edit#installation) 
-# to install the `add` subcommand.
-#
-# Running `cargo add DEPENDENCY_NAME` will
-# add the latest version of a dependency to the list,
-# and it will keep the alphabetic ordering for you.
+[[bin]]
+name = "query-metrics"
+path = "src/cli.rs"
+
+[[bin]]
+name = "query-metrics-lambda"
+path = "src/main.rs"
 
 [dependencies]
 anyhow = "1.0.79"
@@ -21,7 +18,8 @@ aws-sdk-cloudwatch = "1.11.0"
 aws-sdk-config = "1.11.0"
 aws_lambda_events = { version = "0.12.0" }
 base64 = "0.21.7"
-deltalake = { version = "0.16.5", features = ["datafusion", "s3"] }
+deltalake-core = { version = "0.17.0", features = ["datafusion"] }
+deltalake-aws = { version = "0.1.0" }
 
 lambda_runtime = "0.8.3"
 serde = { version = "1.0.195", features = ["derive"] }

lambdas/query-metrics/src/cli.rs

@@ -0,0 +1,97 @@
+///
+/// The CLI helps test a manifest
+///
+use std::collections::HashMap;
+use std::sync::Arc;
+
+use deltalake_core::arrow::util::pretty::print_batches;
+use deltalake_core::arrow::{array::PrimitiveArray, datatypes::Int64Type};
+use deltalake_core::datafusion::common::*;
+use deltalake_core::datafusion::execution::context::SessionContext;
+use tracing::log::*;
+
+mod config;
+
+#[tokio::main]
+async fn main() -> anyhow::Result<()> {
+    deltalake_aws::register_handlers(None);
+
+    tracing_subscriber::fmt()
+        .with_env_filter(tracing_subscriber::EnvFilter::from_default_env())
+        .with_target(true)
+        .init();
+
+    let conf = config::Configuration::from_file("prod-manifest.yml");
+
+    for (name, gauges) in conf.gauges.iter() {
+        for gauge in gauges.iter() {
+            println!("Querying the {name} table");
+            let ctx = SessionContext::new();
+            let table = deltalake_core::open_table(&gauge.url)
+                .await
+                .expect("Failed to register table");
+            println!("table opened");
+            tokio::time::sleep(std::time::Duration::from_secs(5)).await;
+            ctx.register_table("source", Arc::new(table))
+                .expect("Failed to register table with datafusion");
+
+            println!("Running query: {}", gauge.query);
+
+            let df = ctx
+                .sql(&gauge.query)
+                .await
+                .expect("Failed to execute query");
+
+            match gauge.measurement_type {
+                config::Measurement::Count => {
+                    let count = df.count().await.expect("Failed to collect batches");
+                    println!("Counted {count} rows");
+                }
+                config::Measurement::DimensionalCount => {
+                    println!("Need to run dimensional count");
+                    let batches = df.collect().await.expect("Failed to collect batches");
+                    //let batches = df.explain(false, false).unwrap().collect().await.expect("Failed to collect batches");
+                    let _ = print_batches(&batches);
+
+                    println!("I see this many batches: {}", batches.len());
+                    // Interestingly the collect produces a lot of zero row batches
+                    for batch in batches.iter().filter(|b| b.num_rows() > 0) {
+                        if let Some(_counts) = batch.column_by_name("count") {
+                            // Fetching the count column just to ensure that it exists before doing
+                            // any more computation
+                            let schema = batch.schema();
+                            let fields = schema.fields();
+
+                            for row in 0..batch.num_rows() {
+                                let mut dimensions: HashMap<String, String> = HashMap::new();
+                                let mut counted = false;
+                                let mut count = 0;
+
+                                for (idx, column) in batch.columns().iter().enumerate() {
+                                    let field = &fields[idx];
+                                    let name = field.name();
+                                    if name == "count" {
+                                        let arr: &PrimitiveArray<Int64Type> =
+                                            arrow::array::cast::as_primitive_array(&column);
+                                        count = arr.value(row);
+                                        counted = true;
+                                    } else {
+                                        let arr = arrow::array::cast::as_string_array(&column);
+                                        dimensions.insert(name.into(), arr.value(row).into());
+                                    }
+                                }
+
+                                if counted {
+                                    println!("{count}: {dimensions:?}");
+                                }
+                            }
+                        } else {
+                            error!("The result set must have a column named `count`");
+                        }
+                    }
+                }
+            }
+        }
+    }
+    Ok(())
+}

lambdas/query-metrics/src/config.rs

@@ -13,8 +13,7 @@ pub struct Configuration {
 }
 
 impl Configuration {
-    #[cfg(test)]
-    fn from_file<S: Into<String> + AsRef<Path>>(location: S) -> Self {
+    pub fn from_file<S: Into<String> + AsRef<Path>>(location: S) -> Self {
         serde_yaml::from_reader(File::open(location).expect("Failed to open manifest"))
             .expect("Failed to deserialize")
     }
@@ -39,6 +38,7 @@ pub struct Gauge {
 #[serde(rename_all = "lowercase")]
 pub enum Measurement {
     Count,
+    DimensionalCount,
 }
 
 #[cfg(test)]

lambdas/query-metrics/src/main.rs

@@ -5,19 +5,23 @@
 use aws_lambda_events::event::cloudwatch_events::CloudWatchEvent;
 use aws_sdk_cloudwatch::{
     primitives::DateTime,
-    types::{MetricDatum, StandardUnit},
+    types::{Dimension, MetricDatum, StandardUnit},
 };
-use deltalake::datafusion::common::*;
-use deltalake::datafusion::execution::context::SessionContext;
+use deltalake_core::arrow::{array::PrimitiveArray, datatypes::Int64Type};
+use deltalake_core::datafusion::common::*;
+use deltalake_core::datafusion::execution::context::SessionContext;
 use lambda_runtime::{run, service_fn, Error, LambdaEvent};
 use tracing::log::*;
 
+use std::collections::HashMap;
 use std::sync::Arc;
 use std::time::SystemTime;
 
 mod config;
 
 async fn function_handler(_event: LambdaEvent<CloudWatchEvent>) -> Result<(), Error> {
+    deltalake_aws::register_handlers(None);
+
     let aws_config = aws_config::load_defaults(aws_config::BehaviorVersion::latest()).await;
     let cloudwatch = aws_sdk_cloudwatch::Client::new(&aws_config);
 
@@ -31,7 +35,7 @@ async fn function_handler(_event: LambdaEvent<CloudWatchEvent>) -> Result<(), Er
         for gauge in gauges.iter() {
             debug!("Querying the {name} table");
             let ctx = SessionContext::new();
-            let table = deltalake::open_table(&gauge.url)
+            let table = deltalake_core::open_table(&gauge.url)
                 .await
                 .expect("Failed to register table");
             ctx.register_table("source", Arc::new(table))
@@ -64,6 +68,68 @@ async fn function_handler(_event: LambdaEvent<CloudWatchEvent>) -> Result<(), Er
                         .await?;
                     debug!("Result of CloudWatch send: {res:?}");
                 }
+                config::Measurement::DimensionalCount => {
+                    let batches = df.collect().await.expect("Failed to collect batches");
+                    debug!("I see this many batches: {}", batches.len());
+
+                    // Interestingly the collect produces a lot of zero row batches
+                    for batch in batches.iter().filter(|b| b.num_rows() > 0) {
+                        if let Some(_counts) = batch.column_by_name("count") {
+                            // Fetching the count column just to ensure that it exists before doing
+                            // any more computation
+                            let schema = batch.schema();
+                            let fields = schema.fields();
+
+                            for row in 0..batch.num_rows() {
+                                let mut dimensions: HashMap<String, String> = HashMap::new();
+                                let mut counted = false;
+                                let mut count = 0;
+
+                                for (idx, column) in batch.columns().iter().enumerate() {
+                                    let field = &fields[idx];
+                                    let name = field.name();
+                                    if name == "count" {
+                                        let arr: &PrimitiveArray<Int64Type> =
+                                            arrow::array::cast::as_primitive_array(&column);
+                                        count = arr.value(row);
+                                        counted = true;
+                                    } else {
+                                        let arr = arrow::array::cast::as_string_array(&column);
+                                        dimensions.insert(name.into(), arr.value(row).into());
+                                    }
+                                }
+
+                                if counted {
+                                    debug!("{count}: {dimensions:?}");
+                                    let mut dims: Vec<Dimension> = vec![];
+
+                                    for (key, value) in dimensions.iter() {
+                                        dims.push(
+                                            Dimension::builder().name(key).value(value).build(),
+                                        );
+                                    }
+                                    let datum = MetricDatum::builder()
+                                        .metric_name(&gauge.name)
+                                        .timestamp(DateTime::from(SystemTime::now()))
+                                        .set_dimensions(Some(dims))
+                                        .value(count as f64)
+                                        .unit(StandardUnit::Count)
+                                        .build();
+
+                                    let res = cloudwatch
+                                        .put_metric_data()
+                                        .namespace(format!("DataLake/{name}"))
+                                        .metric_data(datum)
+                                        .send()
+                                        .await?;
+                                    debug!("Result of CloudWatch send: {res:?}");
+                                }
+                            }
+                        } else {
+                            error!("The result set must have a column named `count`");
+                        }
+                    }
+                }
             }
         }
     }