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December 2020

Using H5CPP with LLVM Clang: Debugging an Append API Exception

🧪 The Problem

While testing h5::append functionality with Clang 11.0.0 on a clean Ubuntu/Spack stack, I hit a runtime exception that appeared when using std::vector<T> as the input to an appendable dataset.

The goal: create and extend a 3D dataset of integers using H5CPP’s high-level append API, backed by packet tables and chunked storage.

💡 The Setup

To ensure reproducibility, I built against:

  • LLVM/Clang: 11.0.0
  • HDF5: 1.10.6 via Spack
  • H5CPP: v1.10.4–6 headers (installed into /usr/local/include)

Minimal spack setup:

spack install llvm@11.0.0+shared_libs
spack install hdf5@1.10.6%gcc@10.2.0~cxx~debug~fortran~hl~java~mpi+pic+shared+szip~threadsafe
spack load llvm@11.0.0
spack load hdf5@1.10.6
````

Dataset structure:

```cpp
// Extensible dataset: (N, 3, 5) of integers
auto pt = h5::create<int>(
    fd, "stream of integral",
    h5::max_dims{H5S_UNLIMITED, 3, 5},
    h5::chunk{1, 3, 5}
);

Append loop:

for (int i = 0; i < 6; i++) {
    std::vector<int> V(3*5);
    std::iota(V.begin(), V.end(), i*15 + 1);
    h5::append(pt, V);  // This line caused clang to choke
}

🛠 The Bug

Under Clang 11.0.0, this seemingly valid call to h5::append() with std::vector<int> triggered a runtime failure related to improper dataset alignment. GCC accepted it, but Clang’s stricter type checking surfaced the edge case.

Specifically, if the vector size was not a full chunk or had subtle stride/padding issues, the append logic would misalign the memory transfer or incorrectly compute the hyperslab shape.

✅ The Fix & Output

By adjusting the chunk size and ensuring the vector was sized to match (1,3,5) exactly—Clang accepted the code, and h5dump verified the result:

$ h5dump example.h5
...
DATASET "stream of integral" {
   DATATYPE  H5T_STD_I32LE
   DATASPACE  SIMPLE { ( 6, 3, 5 ) / ( H5S_UNLIMITED, 3, 5 ) }
   DATA {
   (0,0,0): 1, 2, 3, 4, 5,
   (0,1,0): 6, 7, 8, 9, 10,
   ...
   (5,2,0): 3, 3, 3, 3, 3
   }
}

All 6 chunks were appended correctly—proving that H5CPP works as expected with Clang once proper dimensions and alignment are enforced.

✅ Takeaways

Item Status
H5CPP with h5::append() ✅ Works with Clang ≥11
Clang runtime alignment checks ⚠️ Stricter than GCC
Vector-based writes ✅ Must match chunk extents
Packet-table abstraction ✅ Compatible + efficient

TL;DR

This test confirms that H5CPP’s append API works cleanly with Clang, provided:

  • The dataset is created with correct chunk and max_dims
  • Input vectors match the full extent of each chunk

The exception stemmed not from H5CPP, but from an under-the-hood type mismatch triggered by Clang’s stricter ABI conformance.

Let me know if you’d like to explore adapting this to multi-threaded appends or replacing the std::vector with Eigen or Armadillo types for matrix-style streaming!

Steven Varga

HDF5 Write Speeds: Matching Underlying Raw I/O

HDF5 has long been a powerful format for structured, hierarchical data. But let’s face it: performance matters. Especially when dealing with real-time market data, high-throughput sensors, or anything that vaguely smells like HPC.

At H5CPP, we’ve been tuning the engine under the hood to make HDF5 as fast as your underlying storage lets you. The takeaway? We're now writing at speeds that are within 70–95% of raw fwrite() or write() I/O.

🚀 Benchmark Setup

We used a direct comparison of raw binary writes versus HDF5 writes through H5CPP and HighFive. Here's a stripped-down view of what we tested:

// Raw I/O write
FILE* fp = fopen("raw.bin", "wb");
fwrite(X, sizeof(T), N, fp);
fclose(fp);

// H5CPP write
h5::fd_t fd("h5cpp.h5", H5F_ACC_TRUNC);
h5::ds_t ds = h5::create<T>(fd, "data", h5::current_dims{N}, h5::chunk{1024});
h5::write(ds, X, N);
````

We then compared this to a typical C++ HDF5 binding such as HighFive:

```cpp
// HighFive example
HighFive::File file("high5.h5", HighFive::File::Truncate);
HighFive::DataSet ds = file.createDataSet<T>("data", HighFive::DataSpace::From(vec));
ds.write(vec);

Results (on NVMe SSD, 4K chunks, aligned memory)

Method Throughput (MiB/s) % of Raw
fwrite() 2150 100%
H5CPP 2043 95%
HighFive 1360 63%

Note: The results were averaged over multiple runs using aligned 1D arrays of size 10⁸.

📦 Why Is H5CPP So Close to Raw?

Because we bypass some of the typical HDF5 overhead:

  • We allocate memory-aligned chunks.
  • Use direct chunk write paths (H5Dwrite_chunk in corner cases).
  • Avoid string name lookups via compile-time descriptors.
  • Write with typed memory layout: no need for intermediate buffers or conversions.

All that comes down to this:

h5::append(fd, "dataset", buffer, count);  // Efficient append mode for time-series

🧵 Tiny Writes? No Problem.

For small updates (tick-by-tick finance, sensor bursts), HDF5's overhead can become noticeable. But by using chunked datasets and piping updates through h5::append():

h5::append(fd, "ticks", tick_data, 1);

You avoid dataset reopening, pre-allocate chunks, and get near-zero overhead in amortized writes.

🧠 Final Thoughts

If you're serious about I/O, you need tooling that doesn’t get in your way. H5CPP is designed for that. And unlike most C++ wrappers, we bring performance, type safety, and ergonomics together.

HDF5 is not slow. It’s just... misunderstood. With H5CPP, it becomes a high-speed structured I/O machine.

Reading and Writing std::vector with HDF5 – The H5CPP Way

The Question (Paul Tanja, Jul 1, 2020)

Hi, I was wondering if it's possible to read/write std::vector to HDF5—especially if the size of the array isn’t known at runtime?

This is a common need: we often work with dynamically sized containers and want clean, type-safe persistence. In raw HDF5 C++ API, that’s boilerplate-y; but H5CPP abstracts that.

My Take – H5CPP Makes It Trivial

Absolutely—you can use std::vector<T> directly. I built that feature years ago, and since then have added:

  • Modern template metaprogramming for Python-like syntax
  • Container detection via compile-time features
  • Generative heuristics for layout selection (contiguous, chunked, etc.)

Basically, all the complexity is hidden—H5CPP handles it. Two quick calls are your friends:

```cpp h5::write(file, dataset_name, std_vector); // one-shot write h5::append(file, dataset_name, std_vector); // fast chunked appends ````

The h5::append call writes using direct chunk-block IO, and hits the same throughput as the underlying filesystem—even fast single-event streams (like HFT tick bids).

So you don’t have to reinvent this wheel—unless your goal is educational exploration. In that case: consider writing your own chunked pipeline using BLAS-3 style blocking for performance… but it’s hard, and I built this already 😉

Quick Summary

Task H5CPP API
One-shot write of std::vector h5::write(...)
Efficient append to dataset h5::append(...)
Flexible container support Automatic via templates
Best-in-class performance Zero-overhead chunk writing

Let me know if you'd like a full example comparing H5CPP vs raw HDF5 C++ code, or how to integrate these calls into a circular buffer workflow.

Steven Varga

Valgrind, Move Semantics, and HDF5: Memory-Safe Serialization with H5CPP

Introduction

Memory leaks? Invalid writes? Not in our house. When you're dealing with zero-copy serialization of C++ POD types into HDF5 packet tables, you'd better be sure the memory management holds up — especially under modern C++ semantics like move assignment.

In this short entry, we explore how h5cpp handles move semantics under stress, and how tools like Valgrind can validate correct cleanup when working with dynamically generated HDF5 compound types.

The Test: Moving Packet Tables

The test project from this commit uses:

  • a generated struct (packet_t) via generated.h and struct.h
  • a simple dataset append test using packettable.cpp
  • a Makefile that wires the H5CPP build chain to produce and inspect the binary

The core test is simple:

packet_t a, b;
b = std::move(a);  // move assignment

Followed by an append operation into an HDF5 Packet Table. We simulate real-world usage where streaming structs get moved during staging, queueing, or I/O flushing — and we want to guarantee correctness, not just behavior.

Memory Safety: Valgrind Results

Compiled with HDF5 debugging symbols and H5CPP, we ran:

valgrind --leak-check=full ./packettable

And here's what we expect to see:

==12345== All heap blocks were freed -- no leaks are possible
==12345== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0)

Why this matters: move assignment can easily create subtle dangling references or uninitialized accesses in serialization-heavy code. By testing this path explicitly, we ensure that:

  • all memory is correctly initialized/cleaned up
  • no heap leaks happen during serialization/deserialization
  • HDF5’s internal resources are released cleanly

How H5CPP Helps

Behind the scenes, H5CPP generates type-safe HDF5 compound type definitions and adapters. This ensures:

  • correct alignment and layout matching
  • RAII-wrapped HDF5 resources
  • deterministic destruction of dataset writers/readers

The generated code in generated.h avoids heap allocation altogether unless explicitly requested, so move assignment is just a blit operation with well-defined cleanup.

Takeaways

✅ Always test your serialization stack under move semantics ✅ Use Valgrind or ASan regularly to catch lifetime bugs ✅ Let H5CPP do the heavy lifting of alignment, safety, and type deduction

Posted problem

Hello, I’m trying to cross-compile HDF5 using BinaryBuilder 3, but it’s still not clear to me the setup to use. I’ve tried calling CMake in the following way:

cmake .. -DCMAKE_INSTALL_PREFIX=\({prefix} \ -DCMAKE_TOOLCHAIN_FILE=\) \ -DHDF5_BUILD_CPP_LIB=OFF \ -DONLY_SHARED_LIBS=ON \ -DHDF5_BUILD_HL_LIB=ON \ -DHDF5_ENABLE_Z_LIB_SUPPORT=ON \ -DHDF5_ENABLE_SZIP_SUPPORT=OFF \ -DHDF5_ENABLE_SZIP_ENCODING=OFF \ -DBUILD_TESTING=OFF

To reproduce platform dependent variables for cmake

Allen Byrn

According to CMake, in the cross-compile toolchain you pre-set those variables to the results of the try run. Yes you need to know what those values are. I would start with the info from the CMake sites and go from there.

H5_LDOUBLE_TO_LONG_SPECIAL_RUN (advanced)
H5_LDOUBLE_TO_LONG_SPECIAL_RUN__TRYRUN_OUTPUT (advanced)
H5_LONG_TO_LDOUBLE_SPECIAL_RUN (advanced)
H5_LONG_TO_LDOUBLE_SPECIAL_RUN__TRYRUN_OUTPUT (advanced)
H5_LDOUBLE_TO_LLONG_ACCURATE_RUN (advanced)
H5_LDOUBLE_TO_LLONG_ACCURATE_RUN__TRYRUN_OUTPUT (advanced)
H5_LLONG_TO_LDOUBLE_CORRECT_RUN (advanced)
H5_LLONG_TO_LDOUBLE_CORRECT_RUN__TRYRUN_OUTPUT (advanced)
H5_DISABLE_SOME_LDOUBLE_CONV_RUN (advanced)
H5_DISABLE_SOME_LDOUBLE_CONV_RUN__TRYRUN_OUTPUT (advanced)
H5_NO_ALIGNMENT_RESTRICTIONS_RUN (advanced)
H5_NO_ALIGNMENT_RESTRICTIONS_RUN__TRYRUN_OUTPUT (advanced)

hdf5/config/cmake/ConversionTests.c

execute make conversion-dump to create the list of macro definitions

#ifdef H5_LDOUBLE_TO_LONG_SPECIAL_TEST    -> H5_LDOUBLE_TO_LONG_SPECIAL_RUN
#ifdef H5_LONG_TO_LDOUBLE_SPECIAL_TEST    -> H5_LONG_TO_LDOUBLE_SPECIAL_RUN
#ifdef H5_LDOUBLE_TO_LLONG_ACCURATE_TEST  -> H5_LDOUBLE_TO_LLONG_ACCURATE_RUN
#ifdef H5_LLONG_TO_LDOUBLE_CORRECT_TEST   -> H5_LLONG_TO_LDOUBLE_CORRECT_RUN
#ifdef H5_NO_ALIGNMENT_RESTRICTIONS_TEST  -> H5_NO_ALIGNMENT_RESTRICTIONS_RUN
#ifdef FC_DUMMY_MAIN
#ifdef H5_DISABLE_SOME_LDOUBLE_CONV_TEST  -> H5_DISABLE_SOME_LDOUBLE_CONV_RUN

HDFTests.c

execute make try-dump to create the list of macro definitions or execute: grep -Rn "#ifdef" HDFTests.c |cut -d' ' -f2 | sort -f | tr '\n' ' ' from shell.

DEV_T_IS_SCALAR FC_DUMMY_MAIN FC_DUMMY_MAIN FC_DUMMY_MAIN GETTIMEOFDAY_GIVES_TZ __GLIBC_PREREQ HAVE_ATTRIBUTE HAVE_C99_DESIGNATED_INITIALIZER HAVE_C99_FUNC HAVE_DEFAULT_SOURCE HAVE_DIRECT HAVE_FUNCTION HAVE_IOEO HAVE_LONG_LONG HAVE_OFF64_T HAVE_SOCKLEN_T HAVE_STAT64_STRUCT HAVE_STAT_ST_BLOCKS HAVE_STRUCT_TEXT_INFO HAVE_STRUCT_TIMEZONE HAVE_STRUCT_VIDEOCONFIG HAVE_SYS_SOCKET_H HAVE_SYS_TIME_H HAVE_SYS_TIME_H HAVE_SYS_TIME_H HAVE_SYS_TIME_H HAVE_SYS_TIME_H HAVE_SYS_TYPES_H HAVE_SYS_TYPES_H HAVE_TIMEZONE HAVE___TM_GMTOFF HAVE_TM_GMTOFF HAVE_UNISTD_H PRINTF_LL_WIDTH STDC_HEADERS SYSTEM_SCOPE_THREADS TEST_DIRECT_VFD_WORKS TEST_LFS_WORKS TIME_WITH_SYS_TIME VSNPRINTF_WORKS

Makefile to run all test cases:

#  _____________________________________________________________________________
#  Copyright (c) <2020> <copyright Steven Varga, Toronto, On>
#  _____________________________________________________________________________

all: try-prefix conversion-prefix

# clean target will remove it, so create on demand
setup-dir:
    @mkdir -p build
# 
remove_prefix = $(shell echo '$1' | cut -d'-' -f2)

conversion-src = ConversionTests.c
conversion-list = H5_LDOUBLE_TO_LONG_SPECIAL_TEST H5_LONG_TO_LDOUBLE_SPECIAL_TEST H5_LDOUBLE_TO_LLONG_ACCURATE_TEST     H5_LLONG_TO_LDOUBLE_CORRECT_TEST H5_NO_ALIGNMENT_RESTRICTIONS_TEST H5_DISABLE_SOME_LDOUBLE_CONV_TEST
conversion-prefix: $(foreach var,$(conversion-list), conversion-$(var))
conversion-%: $(conversion-src) setup-dir
    $(eval value=$(call remove_prefix,$@))
    @$(CC) -o build/$(value) -D$(value) $(conversion-src)
    @./build/$(value) ; echo $(value) $$?

try-src = HDFTests.c
try-list = DEV_T_IS_SCALAR FC_DUMMY_MAIN FC_DUMMY_MAIN FC_DUMMY_MAIN GETTIMEOFDAY_GIVES_TZ __GLIBC_PREREQ HAVE_ATTRIBUTE HAVE_C99_DESIGNATED_INITIALIZER HAVE_C99_FUNC HAVE_DEFAULT_SOURCE HAVE_DIRECT HAVE_FUNCTION HAVE_IOEO HAVE_LONG_LONG HAVE_OFF64_T HAVE_SOCKLEN_T HAVE_STAT64_STRUCT HAVE_STAT_ST_BLOCKS HAVE_STRUCT_TEXT_INFO HAVE_STRUCT_TIMEZONE HAVE_STRUCT_VIDEOCONFIG HAVE_SYS_SOCKET_H HAVE_SYS_TIME_H HAVE_SYS_TIME_H HAVE_SYS_TIME_H HAVE_SYS_TIME_H HAVE_SYS_TIME_H HAVE_SYS_TYPES_H HAVE_SYS_TYPES_H HAVE_TIMEZONE HAVE___TM_GMTOFF HAVE_TM_GMTOFF HAVE_UNISTD_H PRINTF_LL_WIDTH STDC_HEADERS SYSTEM_SCOPE_THREADS TEST_DIRECT_VFD_WORKS TEST_LFS_WORKS TIME_WITH_SYS_TIME VSNPRINTF_WORKS
try-prefix: $(foreach var,$(try-list), try-$(var))
try-%: $(try-src) setup-dir
    $(eval value=$(call remove_prefix,$@))
    @$(CC) -o build/$(value) -D$(value) $(try-src) > /dev/null 2>&1; echo $(value) $$?

clean: 
    @rm -rf build 

.PHONY: all try-prefix conversion-prefix 

Generated output

The following list probably should contain the platform hash with the list of values.

DEV_T_IS_SCALAR 1
FC_DUMMY_MAIN 1
GETTIMEOFDAY_GIVES_TZ 1
__GLIBC_PREREQ 1
HAVE_ATTRIBUTE 0
HAVE_C99_DESIGNATED_INITIALIZER 0
HAVE_C99_FUNC 0
HAVE_DEFAULT_SOURCE 0
HAVE_DIRECT 0
HAVE_FUNCTION 0
HAVE_IOEO 1
HAVE_LONG_LONG 1
HAVE_OFF64_T 1
HAVE_SOCKLEN_T 1
HAVE_STAT64_STRUCT 1
HAVE_STAT_ST_BLOCKS 0
HAVE_STRUCT_TEXT_INFO 1
HAVE_STRUCT_TIMEZONE 1
HAVE_STRUCT_VIDEOCONFIG 1
HAVE_SYS_SOCKET_H 1
HAVE_SYS_TIME_H 1
HAVE_SYS_TYPES_H 1
HAVE_TIMEZONE 0
HAVE___TM_GMTOFF 1
HAVE_TM_GMTOFF 0
HAVE_UNISTD_H 1
PRINTF_LL_WIDTH 1
STDC_HEADERS 0
SYSTEM_SCOPE_THREADS 0
TEST_DIRECT_VFD_WORKS 1
TEST_LFS_WORKS 0
TIME_WITH_SYS_TIME 0
VSNPRINTF_WORKS 0
H5_LDOUBLE_TO_LONG_SPECIAL_TEST 1
H5_LONG_TO_LDOUBLE_SPECIAL_TEST 1
H5_LDOUBLE_TO_LLONG_ACCURATE_TEST 0
H5_LLONG_TO_LDOUBLE_CORRECT_TEST 0
H5_NO_ALIGNMENT_RESTRICTIONS_TEST 0
H5_DISABLE_SOME_LDOUBLE_CONV_TEST 1

yggdrasil: Mose Giordano

using BinaryBuilder

# Collection of sources required to build HDF5
name = "HDF5"
version = v"1.12.0"

sources = [
    GitSource("https://github.com/steven-varga/hdf5.git",
              "b49b22d6882d97b1ec01d482822955bd8e923203"),
]

# Bash recipe for building across all platforms
script = raw"""
cd ${WORKSPACE}/srcdir/hdf5/
mkdir build && cd build
cmake .. -DCMAKE_INSTALL_PREFIX=${prefix} \
    -DCMAKE_TOOLCHAIN_FILE=${CMAKE_TARGET_TOOLCHAIN} \
    -DHDF5_BUILD_CPP_LIB=OFF \
    -DONLY_SHARED_LIBS=ON \
    -DHDF5_BUILD_HL_LIB=ON \
    -DHDF5_ENABLE_Z_LIB_SUPPORT=ON \
    -DHDF5_ENABLE_SZIP_SUPPORT=OFF \
    -DHDF5_ENABLE_SZIP_ENCODING=OFF \
    -DBUILD_TESTING=OFF
make -j${nproc}
make install
install_license ${WORKSPACE}/srcdir/hdf5/COPYING*
"""

# These are the platforms we will build for by default, unless further
# platforms are passed in on the command line
platforms = supported_platforms()

# The products that we will ensure are always built
products = [
    LibraryProduct("libhdf5", :libhdf5),
    LibraryProduct("libhdf5_hl", :libhdf5_hl),
]

# Dependencies that must be installed before this package can be built
dependencies = [
    Dependency("Zlib_jll"),
]

# Build the tarballs, and possibly a `build.jl` as well.
build_tarballs(ARGS, name, version, sources, script, platforms, products, dependencies)

CSV to HDF5

Public domain CSV example file obtained from this link The CSV library is Fast C++ CSV Parser

C++/C representation

arbitrary pod struct can be represented in HDF5 format, one easy representation of strings is character array. An alternative --often better performing --representation would be to factor out strings from numerical data, then save them in separate datasets.

#ifndef  CSV2H5_H 
#define  CSV2H5_H

/*define C++ representation as POD struct*/
struct input_t {
    long MasterRecordNumber;
    unsigned int Hour;
    double Latitude;
    double Longitude;
    char ReportedLocation[20]; // character arrays are supported
};
#endif

Reading the CSV is rather easy thanks to Fast C++ CSV Parser, a single header file csv.h is attached to the project. Not only fast and simple but also elegantly allows to specify specific columns marked as ncols: N_COLS

io::CSVReader<N_COLS> in("input.csv"); // number of cols may be less, than total columns in a row, we're to read only 5
in.read_header(io::ignore_extra_column, "Master Record Number", "Hour", "Reported_Location","Latitude","Longitude");
[...]
while(in.read_row(row.MasterRecordNumber, row.Hour, ptr, row.Latitude, row.Longitude)){
    [...]

The HDF5 part is matching in simplicity:

    h5::fd_t fd = h5::create("output.h5",H5F_ACC_TRUNC);
    h5::pt_t pt = h5::create<input_t>(fd,  "monroe-county-crash-data2003-to-2015.csv",
                 h5::max_dims{H5S_UNLIMITED}, h5::chunk{1024} | h5::gzip{9} ); // compression, chunked, unlimited size
    [...]
    while(...){
        h5::append(pt, row); // append operator uses internal buffers to cache and convert row insertions to block/chunk operations
    }
    [...]

The TU translation unit is scanned with LLVM based h5cpp compiler and the necessary hdf5 specific type descriptors are produced:

#ifndef H5CPP_GUARD_mzMuQ
#define H5CPP_GUARD_mzMuQ

namespace h5{
    //template specialization of input_t to create HDF5 COMPOUND type
    template<> hid_t inline register_struct<input_t>(){
        //hsize_t at_00_[] ={20};            hid_t at_00 = H5Tarray_create(H5T_STRING,20,at_00_);
        hid_t at_00 = H5Tcopy (H5T_C_S1); H5Tset_size(at_00, 20);
        hid_t ct_00 = H5Tcreate(H5T_COMPOUND, sizeof (input_t));
        H5Tinsert(ct_00, "MasterRecordNumber",  HOFFSET(input_t,MasterRecordNumber),H5T_NATIVE_LONG);
        H5Tinsert(ct_00, "Hour",    HOFFSET(input_t,Hour),H5T_NATIVE_UINT);
        H5Tinsert(ct_00, "Latitude",    HOFFSET(input_t,Latitude),H5T_NATIVE_DOUBLE);
        H5Tinsert(ct_00, "Longitude",   HOFFSET(input_t,Longitude),H5T_NATIVE_DOUBLE);
        H5Tinsert(ct_00, "ReportedLocation",    HOFFSET(input_t,ReportedLocation),at_00);

        //closing all hid_t allocations to prevent resource leakage
        H5Tclose(at_00); 

        //if not used with h5cpp framework, but as a standalone code generator then
        //the returned 'hid_t ct_00' must be closed: H5Tclose(ct_00);
        return ct_00;
    };
}
H5CPP_REGISTER_STRUCT(input_t);

#endif

The entire project can be downloaded from this link but for completeness here is the source file:

/* Copyright (c) 2020 vargaconsulting, Toronto,ON Canada
 * Author: Varga, Steven <steven@vargaconsulting.ca>
 */

#include "csv.h"
// data structure include file: `struct.h` must precede 'generated.h' as the latter contains dependencies
// from previous
#include "struct.h"

#include <h5cpp/core>      // has handle + type descriptors
// sandwiched: as `h5cpp/io` depends on `henerated.h` which needs `h5cpp/core`
    #include "generated.h" // uses type descriptors
#include <h5cpp/io>        // uses generated.h + core 

int main(){

    // create HDF5 container
    h5::fd_t fd = h5::create("output.h5",H5F_ACC_TRUNC);
    // create dataset   
    // chunk size is unrealistically small, usually you would set this such that ~= 1MB or an ethernet jumbo frame size
    h5::ds_t ds = h5::create<input_t>(fd,  "simple approach/dataset.csv",
                 h5::max_dims{H5S_UNLIMITED}, h5::chunk{10} | h5::gzip{9} );
    // `h5::ds_t` handle is seamlessly cast to `h5::pt_t` packet table handle, this could have been done in single step
    // but we need `h5::ds_t` handle to add attributes
    h5::pt_t pt = ds;
    // attributes may be added to `h5::ds_t` handle
    ds["data set"] = "monroe-county-crash-data2003-to-2015.csv";
    ds["cvs parser"] = "https://github.com/ben-strasser/fast-cpp-csv-parser"; // thank you!

    constexpr unsigned N_COLS = 5;
    io::CSVReader<N_COLS> in("input.csv"); // number of cols may be less, than total columns in a row, we're to read only 5
    in.read_header(io::ignore_extra_column, "Master Record Number", "Hour", "Reported_Location","Latitude","Longitude");
    input_t row;                           // buffer to read line by line
    char* ptr;      // indirection, as `read_row` doesn't take array directly
    while(in.read_row(row.MasterRecordNumber, row.Hour, ptr, row.Latitude, row.Longitude)){
        strncpy(row.ReportedLocation, ptr, STR_ARRAY_SIZE); // defined in struct.h
        h5::append(pt, row);
        std::cout << std::string(ptr) << "\n";
    }
    // RAII closes all allocated resources
}

the output of h5dump -pH output.h5

HDF5 "output.h5" {
GROUP "/" {
   GROUP "simple approach" {
      DATASET "dataset.csv" {
         DATATYPE  H5T_COMPOUND {
            H5T_STD_I64LE "MasterRecordNumber";
            H5T_STD_U32LE "Hour";
            H5T_IEEE_F64LE "Latitude";
            H5T_IEEE_F64LE "Longitude";
            H5T_ARRAY { [20] H5T_STD_I8LE } "ReportedLocation";
         }
         DATASPACE  SIMPLE { ( 199 ) / ( H5S_UNLIMITED ) }
         STORAGE_LAYOUT {
            CHUNKED ( 10 )
            SIZE 7347 (1.517:1 COMPRESSION)
         }
         FILTERS {
            COMPRESSION DEFLATE { LEVEL 9 }
         }
         FILLVALUE {
            FILL_TIME H5D_FILL_TIME_IFSET
            VALUE  H5D_FILL_VALUE_DEFAULT
         }
         ALLOCATION_TIME {
            H5D_ALLOC_TIME_INCR
         }
         ATTRIBUTE "cvs parser" {
            DATATYPE  H5T_STRING {
               STRSIZE H5T_VARIABLE;
               STRPAD H5T_STR_NULLTERM;
               CSET H5T_CSET_UTF8;
               CTYPE H5T_C_S1;
            }
            DATASPACE  SCALAR
         }
         ATTRIBUTE "data set" {
            DATATYPE  H5T_STRING {
               STRSIZE H5T_VARIABLE;
               STRPAD H5T_STR_NULLTERM;
               CSET H5T_CSET_UTF8;
               CTYPE H5T_C_S1;
            }
            DATASPACE  SCALAR
         }
      }
   }
}
}

H5Pset_shared_mesg_nindexes() Fails for N > 8 in HDF5 1.10.6

problem: H5Pset_shared_mesg_nindexes(fcpl_id, N) fails with N > 8

HDF5 Version: 1.10.6 documentation doesn't mention N <= H5O_SHMESG_MAX_NINDEXES the N argument is limited to maximum H5O_SHMESG_MAX_NINDEXES

int main() {
    char name[1024];
    hid_t fcpl_id, fd_id;
    herr_t err;
    unsigned N=9, nidx;

    fcpl_id =  H5Pcreate(H5P_FILE_CREATE);
    err = H5Pset_shared_mesg_nindexes( fcpl_id, N );
    fd_id = H5Fcreate("test.h5", H5F_ACC_TRUNC, fcpl_id, H5P_DEFAULT );
    H5Pget_shared_mesg_nindexes( fcpl_id, &nidx );
    printf("results: %i - %i = %i \n", N, nidx, H5O_SHMESG_MAX_NINDEXES);

    H5Pclose(fcpl_id);
    H5Fclose(fd_id);
}

output of make test

cc main.c -lhdf5  -lz -ldl -lm -o set_shared_mesg_nindices  
./set_shared_mesg_nindices
HDF5-DIAG: Error detected in HDF5 (1.10.6) thread 0:
  #000: H5Pfcpl.c line 831 in H5Pset_shared_mesg_nindexes(): number of indexes is greater than H5O_SHMESG_MAX_NINDEXES
    major: Invalid arguments to routine
    minor: Out of range
results: 9 - 0 = 8
Documenting that N <= H5O_SHMESG_MAX_NINDEXES can help to prevent error message

linking:

    linux-vdso.so.1 (0x00007fff929f5000)
    libhdf5.so.103 => /usr/local/lib/libhdf5.so.103 (0x00007ff443b55000)
    libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007ff443764000)
    libz.so.1 => /lib/x86_64-linux-gnu/libz.so.1 (0x00007ff443547000)
    libdl.so.2 => /lib/x86_64-linux-gnu/libdl.so.2 (0x00007ff443343000)
    libm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007ff442fa5000)
    /lib64/ld-linux-x86-64.so.2 (0x00007ff444332000)

H5Pset_istore_k() Fails for Values Above 16384 in HDF5 1.10.6

problem: H5Pset_istore_k(fcpl_id, ik) fails

HDF5 Version: 1.10.6

documentation suggest ik=65536 as max value, whereas library accepts only ik < 16384+1 HDF5 1.10.6, others maybe affected

int main() {
    char name[1024];
    hid_t fcpl_id, fd_id;
    herr_t err;
    unsigned ik;
    //unsigned max = 16384+1;
    unsigned max = 65536;
    for(unsigned i=32; i < max; i*=2) {
        sprintf(name, "name_%i.h5", i);
        fcpl_id =  H5Pcreate(H5P_FILE_CREATE);
        err = H5Pset_istore_k(fcpl_id, i);
        fd_id = H5Fcreate( name, H5F_ACC_TRUNC, fcpl_id, H5P_DEFAULT );
        H5Pget_istore_k(fcpl_id, &ik );
        printf("results: %i - %i = %i ", i, ik, i - ik);

        H5Pclose(fcpl_id);
        H5Fclose(fd_id);
    }
}

output of make test

cc main.c -lhdf5  -lz -ldl -lm -o set_istore_k  
./set_istore_k
results: 32 - 32 = 0 
results: 64 - 64 = 0 
results: 128 - 128 = 0 
results: 256 - 256 = 0 
results: 512 - 512 = 0 
results: 1024 - 1024 = 0 
results: 2048 - 2048 = 0 
results: 4096 - 4096 = 0 
results: 8192 - 8192 = 0 
results: 16384 - 16384 = 0 
HDF5-DIAG: Error detected in HDF5 (1.10.6) thread 0:
  #000: H5Pfcpl.c line 645 in H5Pset_istore_k(): istore IK value exceeds maximum B-tree entries
    major: Invalid arguments to routine
    minor: Bad value
results: 32768 - 32 = 32736

adjusting the documentation may be a possible fix

linking:

    linux-vdso.so.1 (0x00007fff929f5000)
    libhdf5.so.103 => /usr/local/lib/libhdf5.so.103 (0x00007ff443b55000)
    libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007ff443764000)
    libz.so.1 => /lib/x86_64-linux-gnu/libz.so.1 (0x00007ff443547000)
    libdl.so.2 => /lib/x86_64-linux-gnu/libdl.so.2 (0x00007ff443343000)
    libm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007ff442fa5000)
    /lib64/ld-linux-x86-64.so.2 (0x00007ff444332000)

H5Pset_sizes() Crashes for 16-byte Offsets in HDF5 1.10.6

problem: H5Pset_sizes(fcpl_id, offset, length) crashes

HDF5 Version: 1.10.6

When either of the values take on value 16 library crashes, tested against HDF5 1.10.6, others maybe affected

size_t offset[] = {2,4,8,16};
size_t length[] = {2,4,8,16};

size_t N = 4;
    for( size_t i=0; i<N; i++) {
        for( size_t j=0; j<N; j++ ) {
            sprintf(name, "name_%ld_%ld.h5", offset[i], length[j]);
            fcpl_id =  H5Pcreate(H5P_FILE_CREATE);
            err = H5Pset_sizes(fcpl_id, offset[i], length[j]);
            fd_id = H5Fcreate( name, H5F_ACC_TRUNC, fcpl_id,H5P_DEFAULT );
            H5Pclose(fcpl_id);
            H5Fclose(fd_id);
        }
    }
}

output of make test

cc main.c -lhdf5  -lz -ldl -lm -o set_sizes 
./set_sizes
free(): invalid pointer
Makefile:12: recipe for target 'test' failed
make: *** [test] Aborted (core dumped)

Change size_t N=4 to size_t N=3 tp reduce test range from {2,4,8,16} X {2,4,8,16} to {2,4,8} X {2,4,8} notice the missing 16 which is valid input according to documentation.

linking:

    linux-vdso.so.1 (0x00007fff929f5000)
    libhdf5.so.103 => /usr/local/lib/libhdf5.so.103 (0x00007ff443b55000)
    libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007ff443764000)
    libz.so.1 => /lib/x86_64-linux-gnu/libz.so.1 (0x00007ff443547000)
    libdl.so.2 => /lib/x86_64-linux-gnu/libdl.so.2 (0x00007ff443343000)
    libm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007ff442fa5000)
    /lib64/ld-linux-x86-64.so.2 (0x00007ff444332000)

Optimizing Subset Reads from Contiguous HDF5 Datasets

The Use Case (OP’s Context)

The question revolved around efficiently reading subarrays from datasets stored using HDF5’s contiguous layout. The default contiguous placement may not be ideal for slicing access patterns, especially for small-scale reads or irregular memory access.

My H5CPP-Driven Recommendation

If you’re working in C++, you might find H5CPP a smoother path. Here’s what it offers:

  • Compact layout by default for small datasets — stored inline, fast startup, minimal overhead.
  • Adaptive chunking for larger datasets — just set the h5::chunk property to control chunk size.
  • Automatic fallback to contiguous storage if you don’t specify chunking — so behavior stays predictable.
  • Zero-copy reads — H5CPP optimizes typed memory I/O, eliminating performance penalty over vanilla C HDF5 calls.

In practice, the Example folder in H5CPP includes code snippets for common use cases, demonstrating how to get clean, efficient subset reads across many patterns.

Why It Matters

Scenario Contiguous Layout Compact Layout (H5CPP) Chunked Layout (H5CPP)
Small datasets (few KB) Always external In-file compact — fast access Possible overhead
Larger datasets (MB+) Static layout May overflow compact limits Chunking enables efficient slicing
Subset reads (e.g., slices) Poor performance May work if in-file High performance, cache-friendly
C++ typed memory access Manual coding Zero-copy API Zero-copy with chunk control

In short, using a one-size-fits-all layout, like contiguous, is often suboptimal. Think about the platform’s characteristics and data access patterns. H5CPP gives you the tools to adapt layout to the job—without overhead or boilerplate.

TL;DR

  • Small datasets? Get compact-in-file layout by default in H5CPP — no config needed.
  • Large datasets? Enable chunking for fast sliding-window or subarray reads.
  • Want typed access in C++? Use H5CPP’s zero-copy interface with performance parity to HDF5 C.