Added support to read parquet row groups in chunks

Cargo.toml

@@ -92,7 +92,7 @@ flate2 = "1"
 doc-comment = "0.3"
 crossbeam-channel = "0.5.1"
 # used to test async readers
-tokio = { version = "1", features = ["macros", "rt", "fs"] }
+tokio = { version = "1", features = ["macros", "rt", "fs", "io-util"] }
 tokio-util = { version = "0.6", features = ["compat"] }
 # used to run formal property testing
 proptest = { version = "1", default_features = false, features = ["std"] }
@@ -134,6 +134,8 @@ io_ipc = ["arrow-format"]
 io_ipc_write_async = ["io_ipc", "futures"]
 io_ipc_compression = ["lz4", "zstd"]
 io_flight = ["io_ipc", "arrow-format/flight-data"]
+# base64 + io_ipc because arrow schemas are stored as base64-encoded ipc format.
+io_parquet = ["parquet2", "io_ipc", "base64", "futures", "streaming-iterator", "fallible-streaming-iterator"]
 io_parquet_compression = [
     "parquet2/zstd",
     "parquet2/snappy",
@@ -207,8 +209,6 @@ compute = [
     "compute_lower",
     "compute_upper"
 ]
-# base64 + io_ipc because arrow schemas are stored as base64-encoded ipc format.
-io_parquet = ["parquet2", "io_ipc", "base64", "futures"]
 benchmarks = ["rand"]
 simd = ["packed_simd"]
 

benches/read_parquet.rs

@@ -35,7 +35,7 @@ fn to_buffer(
 fn read_batch(buffer: &[u8], size: usize, column: usize) -> Result<()> {
     let file = Cursor::new(buffer);
 
-    let reader = read::RecordReader::try_new(file, Some(vec![column]), None, None, None)?;
+    let reader = read::FileReader::try_new(file, Some(&[column]), None, None, None)?;
 
     for maybe_chunk in reader {
         let columns = maybe_chunk?;

examples/parquet_read.rs

@@ -1,46 +1,23 @@
 use std::fs::File;
-use std::io::BufReader;
+use std::time::SystemTime;
 
+use arrow2::error::Result;
 use arrow2::io::parquet::read;
-use arrow2::{array::Array, error::Result};
-
-fn read_field(path: &str, row_group: usize, field: usize) -> Result<Box<dyn Array>> {
-    // Open a file, a common operation in Rust
-    let mut file = BufReader::new(File::open(path)?);
-
-    // Read the files' metadata. This has a small IO cost because it requires seeking to the end
-    // of the file to read its footer.
-    let metadata = read::read_metadata(&mut file)?;
-
-    // Convert the files' metadata into an arrow schema. This is CPU-only and amounts to
-    // parse thrift if the arrow format is available on a key, or infering the arrow schema from
-    // the parquet's physical, converted and logical types.
-    let arrow_schema = read::get_schema(&metadata)?;
-
-    // Created an iterator of column chunks. Each iteration
-    // yields an iterator of compressed pages. There is almost no CPU work in iterating.
-    let columns = read::get_column_iterator(&mut file, &metadata, row_group, field, None, vec![]);
-
-    // get the columns' field
-    let field = &arrow_schema.fields[field];
-
-    // This is the actual work. In this case, pages are read and
-    // decompressed, decoded and deserialized to arrow.
-    // Because `columns` is an iterator, it uses a combination of IO and CPU.
-    let (array, _, _) = read::column_iter_to_array(columns, field, vec![])?;
-
-    Ok(array)
-}
 
 fn main() -> Result<()> {
     use std::env;
     let args: Vec<String> = env::args().collect();
 
     let file_path = &args[1];
-    let field = args[2].parse::<usize>().unwrap();
-    let row_group = args[3].parse::<usize>().unwrap();
 
-    let array = read_field(file_path, row_group, field)?;
-    println!("{:?}", array);
+    let reader = File::open(file_path)?;
+    let reader = read::FileReader::try_new(reader, None, None, None, None)?;
+
+    let start = SystemTime::now();
+    for maybe_chunk in reader {
+        let columns = maybe_chunk?;
+        assert!(!columns.is_empty());
+    }
+    println!("took: {} ms", start.elapsed().unwrap().as_millis());
     Ok(())
 }

examples/parquet_read_async.rs

@@ -0,0 +1,57 @@
+use std::sync::Arc;
+use std::time::SystemTime;
+
+use futures::future::BoxFuture;
+use futures::FutureExt;
+use tokio;
+use tokio::fs::File;
+use tokio::io::BufReader;
+use tokio_util::compat::*;
+
+use arrow2::error::Result;
+use arrow2::io::parquet::read::{self, RowGroupDeserializer};
+
+#[tokio::main(flavor = "current_thread")]
+async fn main() -> Result<()> {
+    let start = SystemTime::now();
+
+    use std::env;
+    let args: Vec<String> = env::args().collect();
+    let file_path = Arc::new(args[1].clone());
+
+    // # Read metadata
+    let mut reader = BufReader::new(File::open(file_path.as_ref()).await?).compat();
+
+    // this operation is usually done before reading the data, during planning.
+    // This is a mix of IO and CPU-bounded tasks but both of them are O(1)
+    let metadata = read::read_metadata_async(&mut reader).await?;
+    let schema = read::get_schema(&metadata)?;
+
+    // This factory yields one file descriptor per column and is used to read columns concurrently.
+    // They do not need to be buffered since we execute exactly 1 seek and 1 read on them.
+    let factory = || {
+        Box::pin(async { Ok(File::open(file_path.clone().as_ref()).await?.compat()) })
+            as BoxFuture<_>
+    };
+
+    // This is the row group loop. Groups can be skipped based on the statistics they carry.
+    for row_group in &metadata.row_groups {
+        // A row group is consumed in two steps: the first step is to read the (compressed)
+        // columns into memory, which is IO-bounded.
+        let column_chunks =
+            read::read_columns_async(factory, row_group, schema.fields.clone(), None).await?;
+
+        // the second step is to iterate over the columns in chunks.
+        // this operation is CPU-bounded and should be sent to a separate thread pool (e.g. `tokio_rayon`) to not block
+        // the runtime.
+        // Furthermore, this operation is trivially paralellizable e.g. via rayon, as each iterator
+        // can be advanced in parallel (parallel decompression and deserialization).
+        let chunks = RowGroupDeserializer::new(column_chunks, row_group.num_rows() as usize, None);
+        for maybe_chunk in chunks {
+            let chunk = maybe_chunk?;
+            println!("{}", chunk.len());
+        }
+    }
+    println!("took: {} ms", start.elapsed().unwrap().as_millis());
+    Ok(())
+}

examples/parquet_read_parallel/src/main.rs

@@ -6,61 +6,29 @@ use std::time::SystemTime;
 
 use rayon::prelude::*;
 
-use arrow2::{
-    array::Array, chunk::Chunk, error::Result, io::parquet::read,
-    io::parquet::read::MutStreamingIterator,
-};
+use arrow2::{array::Array, chunk::Chunk, error::Result, io::parquet::read};
 
 fn parallel_read(path: &str, row_group: usize) -> Result<Chunk<Arc<dyn Array>>> {
     let mut file = BufReader::new(File::open(path)?);
-    let file_metadata = read::read_metadata(&mut file)?;
-    let schema = read::get_schema(&file_metadata)?;
+    let metadata = read::read_metadata(&mut file)?;
+    let schema = read::get_schema(&metadata)?;
 
-    // IO-bounded
-    let columns = file_metadata
-        .schema()
-        .fields()
-        .iter()
-        .enumerate()
-        .map(|(field_i, field)| {
-            let start = SystemTime::now();
-            println!("read start - field: {}", field_i);
-            let mut columns = read::get_column_iterator(
-                &mut file,
-                &file_metadata,
-                row_group,
-                field_i,
-                None,
-                vec![],
-            );
-
-            let mut column_chunks = vec![];
-            while let read::State::Some(mut new_iter) = columns.advance().unwrap() {
-                if let Some((pages, metadata)) = new_iter.get() {
-                    let pages = pages.collect::<Vec<_>>();
-
-                    column_chunks.push((pages, metadata.clone()));
-                }
-                columns = new_iter;
-            }
-            println!(
-                "read end - {:?}: {} {}",
-                start.elapsed().unwrap(),
-                field_i,
-                row_group
-            );
-            (field_i, field.clone(), column_chunks)
-        })
-        .collect::<Vec<_>>();
+    // read (IO-bounded) all columns into memory (use a subset of the fields to project)
+    let columns = read::read_columns(
+        &mut file,
+        &metadata.row_groups[row_group],
+        schema.fields,
+        None,
+    )?;
 
     // CPU-bounded
     let columns = columns
         .into_par_iter()
-        .map(|(field_i, parquet_field, column_chunks)| {
-            let columns = read::ReadColumnIterator::new(parquet_field, column_chunks);
-            let field = &schema.fields()[field_i];
-
-            read::column_iter_to_array(columns, field, vec![]).map(|x| x.0.into())
+        .map(|mut iter| {
+            // when chunk_size != None, `iter` must be iterated multiple times to get all the chunks
+            // see the implementation of `arrow2::io::parquet::read::RowGroupDeserializer::next`
+            // to see how this can be done.
+            iter.next().unwrap()
         })
         .collect::<Result<Vec<_>>>()?;
 

src/array/struct_/mod.rs

@@ -74,6 +74,13 @@ impl StructArray {
         let fields = Self::get_fields(&data_type);
         assert!(!fields.is_empty());
         assert_eq!(fields.len(), values.len());
+        assert!(
+            fields
+                .iter()
+                .map(|f| f.data_type())
+                .eq(values.iter().map(|a| a.data_type())),
+            "The fields' datatypes must equal the values datatypes"
+        );
         assert!(values.iter().all(|x| x.len() == values[0].len()));
         if let Some(ref validity) = validity {
             assert_eq!(values[0].len(), validity.len());