Add support for structured dtypes to zarr3 driver, open zarr 2 and 3 structs as void

[BrianMichell]

Resolves#241

This PR implements support for structured data in the zarr3 driver as well as the ability to interact with the full array as raw bytes to avoid needing to manage multiple Stores in client code. I am also happy to revisit the question of #125 for a more general Zarr support for this behavior. I have also implemented the open_as_void feature in the zarr driver for feature parity between the formats.

Below is an example workflow that this usecase would support.

The Python snippet will generate a structured array of several different elements and types. A real world example of this is creating an array to store the SEG-Y 240-byte Trace Headers.

Click here to expand Python example.

importnumpyasnpimportzarrstore="foo.zarr"np_dtype=np.dtype(
[
("field_1", "<i4"),
("field_2", ">i4"),
# More theoretical fields here...
("field_n", "<f4"),
]
)
z=zarr.create_array(
store=store,
shape=(128, 128),
dtype=np_dtype,
chunks=(32, 32),
)
arr=np.zeros((128, 128), dtype=np_dtype)
f1=np.arange(128, dtype="<i4")
# NOTE: We populate f2 as little endian for the demonstrationf2=np.arange(128, dtype="<i4")
fn=np.arange(128, dtype="<f4") /10arr["field_1"][:] =f1[:, None]
arr["field_2"][:] =f2[:, None]
arr["field_n"][:] =fn[:, None]
z[:] =arr

The usecase continues with an HPC C++ application consuming either a single field from the structured dtype, or the entirety of the dtype to perform its intended workload. To keep this example code simple, I only demonstrate consuming the same field out of the raw data, however this could reasonably be extended to 70+ fields per element if writing a trivial data loader.

Click here to expand C++ example.

// Minimal example demonstrating structured Zarr v3 data reading.// This example reads the 'foo.zarr' output generated by generate_struct.py//// Usage:// ./read_foo_zarr --zarr_path=/path/to/foo.zarr
#include<stdint.h>
#include<iostream>
#include<string>
#include<cstring>
#include"absl/flags/flag.h"
#include"absl/flags/parse.h"
#include"absl/status/status.h"
#include<nlohmann/json.hpp>
#include"tensorstore/array.h"
#include"tensorstore/context.h"
#include"tensorstore/data_type.h"
#include"tensorstore/index.h"
#include"tensorstore/open.h"
#include"tensorstore/open_mode.h"
#include"tensorstore/spec.h"
#include"tensorstore/tensorstore.h"
#include"tensorstore/util/result.h"
#include"tensorstore/util/status.h"ABSL_FLAG(std::string, zarr_path,
"/foo.zarr",
"Path to the foo.zarr directory");
namespace {
using ::tensorstore::Index;
// Helper function to read and display data from a tensorstore// T: the logical value type you want to interpret the data as// offset_bytes:// -1 (default): interpret array.data() as a T* over the logical elements// >=0 : treat array.data() as a byte buffer and start reading T// values at `offset_bytes`, stepping by sizeof(T).template <typename T>
absl::Status ReadAndDisplayData(const tensorstore::TensorStore<>& store,
const std::string& description,
Index offset_bytes = -1) {
std::cout << "\n=== " << description << " ===" << std::endl;
// Get array informationauto domain = store.domain();
std::cout << "Domain: " << domain << std::endl;
std::cout << "Data type: " << store.dtype() << std::endl;
// Read all dataTENSORSTORE_ASSIGN_OR_RETURN(
auto array,
tensorstore::Read<tensorstore::zero_origin>(store).result());
std::cout << "Successfully read array with " << array.num_elements()
<< " elements" << std::endl;
auto shape = domain.shape();
if (shape.size() < 2) {
std::cout << "Rank < 2, skipping pretty-print of first 2D block."
<< std::endl;
returnabsl::OkStatus();
}
Index rows = std::min(shape[0], Index{5});
Index cols = std::min(shape[1], Index{5});
std::cout << "First " << rows << "x" << cols << " elements";
if (offset_bytes >= 0) {
std::cout << " starting at byte offset " << offset_bytes;
}
std::cout << " interpreted as " << sizeof(T) * 8 << "-bit values:"
<< std::endl;
// No offset: “normal” interpretation as T[]if (offset_bytes < 0) {
const T* data = reinterpret_cast<const T*>(array.data());
for (Index i = 0; i < rows; ++i) {
for (Index j = 0; j < cols; ++j) {
Index idx = i * shape[1] + j;
if (idx >= array.num_elements()) break;
std::cout << data[idx] << "\t";
}
std::cout << std::endl;
}
returnabsl::OkStatus();
}
// Offset mode: interpret as raw bytes and then per-record struct.constauto* bytes = reinterpret_cast<constuint8_t*>(array.data());
// Total bytes in buffer (works for any dtype).
std::size_t dtype_size = store.dtype().size();
Index total_bytes =
static_cast<Index>(array.num_elements()) *
static_cast<Index>(dtype_size == 0 ? 1 : dtype_size);
if (total_bytes == 0) {
std::cout << "[empty buffer]" << std::endl;
returnabsl::OkStatus();
}
// Determine "record" size in bytes.//// For open_as_void on a structured array, we get something like:// shape = [N0, N1, record_bytes]// dtype = byte//// In that case, treat the last dimension as the record layout.
Index record_bytes = static_cast<Index>(store.dtype().size());
if (store.dtype() == tensorstore::dtype_v<tensorstore::dtypes::byte_t> &&
shape.size() >= 3) {
record_bytes = shape.back(); // e.g. 12 bytes in your example
}
if (offset_bytes < 0 || offset_bytes >= record_bytes) {
std::cout << "[offset outside record size (" << record_bytes
<< " bytes); nothing to display]" << std::endl;
returnabsl::OkStatus();
}
for (Index i = 0; i < rows; ++i) {
for (Index j = 0; j < cols; ++j) {
Index record_index_2d = i * shape[1] + j;
Index base_offset =
record_index_2d * record_bytes + offset_bytes;
if (base_offset + static_cast<Index>(sizeof(T)) > total_bytes) {
std::cout << "[out]\t";
continue;
}
T value{};
std::memcpy(&value,
bytes + base_offset,
sizeof(T));
std::cout << value << "\t";
}
std::cout << std::endl;
}
returnabsl::OkStatus();
}
absl::Status Run(const std::string& zarr_path) {
std::cout << "=== Structured Zarr v3 Example ===" << std::endl;
std::cout << "Reading from: " << zarr_path << std::endl;
auto context = tensorstore::Context::Default();
// Open the structured Zarr v3 array with field access
::nlohmann::json spec = ::nlohmann::json::object();
spec["driver"] = "zarr3";
spec["kvstore"] = ::nlohmann::json::object();
spec["kvstore"]["driver"] = "file";
spec["kvstore"]["path"] = zarr_path + "/";
spec["field"] = "field_2";
auto open_result = tensorstore::Open(spec, context, tensorstore::OpenMode::open,
tensorstore::ReadWriteMode::read).result();
if (!open_result.ok()) {
std::cout << "Failed to open structured array: " << open_result.status() << std::endl;
return open_result.status();
}
auto store = std::move(open_result).value();
TENSORSTORE_RETURN_IF_ERROR(ReadAndDisplayData<int32_t>(store, "Structured Array"));
::nlohmann::json void_spec = spec;
void_spec["open_as_void"] = true;
void_spec.erase("field");
auto void_open_result = tensorstore::Open(void_spec, context, tensorstore::OpenMode::open,
tensorstore::ReadWriteMode::read).result();
if (!void_open_result.ok()) {
std::cout << "Failed to open with open_as_void: " << void_open_result.status() << std::endl;
return void_open_result.status();
}
auto void_store = std::move(void_open_result).value();
constexpr Index kFieldOffsetBytes = 4;
TENSORSTORE_RETURN_IF_ERROR(ReadAndDisplayData<int32_t>(void_store, "Raw Bytes (open_as_void)", kFieldOffsetBytes));
returnabsl::OkStatus();
}
} // namespaceintmain(int argc, char** argv) {
absl::ParseCommandLine(argc, argv);
std::string zarr_path = absl::GetFlag(FLAGS_zarr_path);
if (zarr_path.empty()) {
std::cerr << "Error: --zarr_path is required" << std::endl;
return1;
}
auto status = Run(zarr_path);
if (!status.ok()) {
std::cerr << "\nFinal status: " << status << std::endl;
return1;
}
return0;
}

[BrianMichell]

@jbms@laramiel It would be great to get some feedback on this feature. I'm happy to work through any feedback as merging this is the last missing requirement for our usecase.

[laramiel]

I'll try to begin looking at this PR this week.

[laramiel]

Ok, two high level comments.

1. Remove the default values for open_as_void in the C++ function declarations & definitions.
   bool open_as_void = false => bool open_as_void

2. Can you split this into separate PRs for zarr2 open_as_void and zarr3.

3. In zarr2, it looks like a lot of the cache methods are ~completely different for open_as_void, so it might make sense to create a derived DataCache which is used for open_as_void.

[BrianMichell]

Thanks for the feedback! I've split the features into zarr2 open_as_void feature and zarr3 add structs and open_as_void feature.