GH-348: [Release] Update versions for 16.0.0 (#357)
Closes https://github.com/apache/arrow-cookbook/issues/348
This PR:
- Updates versions from 15.0.0 to 16.0.0
- Updates the R cookbook to pass under 16.0.0
- Updates the C++ cookbook to pass under 16.0.0
- Changes `test_cpp_cookbook.yml` to now always test against stable libarrow.
This is a workaround for not having an up-to-date
conda-nightlies channel, see
https://github.com/apache/arrow/issues/41856
- Adds a note in the top-level README about the status of the dev cookbooks
diff --git a/.github/workflows/test_cpp_cookbook.yml b/.github/workflows/test_cpp_cookbook.yml
index 82f91e9..2b87016 100644
--- a/.github/workflows/test_cpp_cookbook.yml
+++ b/.github/workflows/test_cpp_cookbook.yml
@@ -48,16 +48,7 @@
path: ~/conda_pkgs_dir
key:
${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles('cpp/conda-linux-64.lock') }}
- - name: Setup latest environment
- if: github.event.pull_request.base.ref == 'main'
- uses: conda-incubator/setup-miniconda@v2
- with:
- auto-update-conda: true
- activate-environment: cookbook-cpp-dev
- environment-file: cpp/dev.yml
- auto-activate-base: false
- name: Setup stable environment
- if: github.event.pull_request.base.ref == 'stable'
uses: conda-incubator/setup-miniconda@v2
with:
auto-update-conda: true
diff --git a/README.rst b/README.rst
index 44db6dd..3ec6ba4 100644
--- a/README.rst
+++ b/README.rst
@@ -44,6 +44,8 @@
latest stable version of Apache Arrow.
See https://arrow.apache.org/cookbook/dev for the latest published version using
the development version of Apache Arrow.
+Please note that the development version of the cookbook will be out of date
+while we work to rebuild some of our nightly build infrastructure.
Building All Cookbooks
----------------------
diff --git a/cpp/code/CMakeLists.txt b/cpp/code/CMakeLists.txt
index 00b6976..b92a229 100644
--- a/cpp/code/CMakeLists.txt
+++ b/cpp/code/CMakeLists.txt
@@ -77,7 +77,6 @@
# Add protobuf to flight
-find_package(Protobuf REQUIRED)
find_package(gRPC CONFIG REQUIRED)
find_package(Threads)
diff --git a/cpp/conda-linux-64.lock b/cpp/conda-linux-64.lock
index b55a749..c6fe76a 100644
--- a/cpp/conda-linux-64.lock
+++ b/cpp/conda-linux-64.lock
@@ -1,149 +1,153 @@
# Generated by conda-lock.
# platform: linux-64
-# input_hash: 06946b9926dc3916f67719909745d5ce09df72a0b6305fdfd8a2e35f779591e9
+# input_hash: 4f6db0934a27d87a3c6e5e7678c71c31768c246297b968986f8b40c413067cd0
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diff --git a/cpp/conda-linux-aarch64.lock b/cpp/conda-linux-aarch64.lock
index e4ab825..53e812a 100644
--- a/cpp/conda-linux-aarch64.lock
+++ b/cpp/conda-linux-aarch64.lock
@@ -1,149 +1,152 @@
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# platform: linux-aarch64
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diff --git a/cpp/conda-osx-arm64.lock b/cpp/conda-osx-arm64.lock
index 67af99b..59ca299 100644
--- a/cpp/conda-osx-arm64.lock
+++ b/cpp/conda-osx-arm64.lock
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+https://conda.anaconda.org/conda-forge/osx-arm64/clang-17.0.6-default_h360f5da_7.conda#c98bdbd4985530fac68ea4831d053ba1
+https://conda.anaconda.org/conda-forge/osx-arm64/clang-format-17.0.6-default_h146c034_7.conda#c84655caaad1fe868ff3c9d14f9b11b6
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+https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py39hcf1bb16_1.conda#8cbaf074d564f304ae7fd29ba39184be
+https://conda.anaconda.org/conda-forge/osx-arm64/aws-crt-cpp-0.26.8-h5cd1ed2_11.conda#cceb6e6316256f11d2b975bb4e00dd9d
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+https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.23.0-h8a76758_1.conda#356c74978867e07e12a939a092dcf30d
https://conda.anaconda.org/conda-forge/osx-arm64/numpy-1.26.4-py39h7aa2656_0.conda#c027ed77947314469686cff520a71e5f
-https://conda.anaconda.org/conda-forge/osx-arm64/aws-sdk-cpp-1.11.267-h2fb64bc_3.conda#e11e8d3c0ca63039e4b8101a5063fa30
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-https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-15.0.2-h8eee870_0_cpu.conda#9091cceb2b8fe58b50971e2d92d76239
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-https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-flight-15.0.2-h1f98dca_0_cpu.conda#733adb80c56aa73c5ccf5b26516d1305
-https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-gandiva-15.0.2-h2c81988_0_cpu.conda#8a195c1f905e3799085833e71342a6c7
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-https://conda.anaconda.org/conda-forge/osx-arm64/clang_osx-arm64-16.0.6-h54d7cd3_10.conda#37a978f8023725b8c23c2dfe7de5e7a6
-https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-dataset-15.0.2-hebf3989_0_cpu.conda#95b177dc1112842d3f768c28d1f47852
-https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-flight-sql-15.0.2-hb095944_0_cpu.conda#6c7c707f155569a2827ba90ae2472ee9
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+https://conda.anaconda.org/conda-forge/osx-arm64/clangxx_impl_osx-arm64-17.0.6-h50f59cd_21.conda#a78eb1ab3aad5251ff9c6da5a99d59be
+https://conda.anaconda.org/conda-forge/osx-arm64/gfortran_osx-arm64-13.2.0-h57527a5_1.conda#13ca786286ed5efc9dc75f64b5101210
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+https://conda.anaconda.org/conda-forge/osx-arm64/cxx-compiler-1.8.0-h18dbf2f_1.conda#a1bc5417ab20b451ee141ca3290df479
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+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_0.conda#d6e5ea5fe00164ac6c2dcc5d76a42192
+https://conda.anaconda.org/conda-forge/noarch/sphinx-7.4.7-pyhd8ed1ab_0.conda#c568e260463da2528ecfd7c5a0b41bbd
https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda#e507335cb4ca9cff4c3d0fa9cdab255e
diff --git a/cpp/environment.yml b/cpp/environment.yml
index 6233619..2c94fda 100644
--- a/cpp/environment.yml
+++ b/cpp/environment.yml
@@ -20,10 +20,11 @@
dependencies:
- python=3.9
- compilers
- - libarrow==15.0.2
+ - libarrow==16.0.0
+ - libarrow-flight==16.0.0
- sphinx
- gtest
- gmock
- - pyarrow==15.0.2
+ - pyarrow==16.0.0
- clang-tools
- zlib
diff --git a/java/source/conf.py b/java/source/conf.py
index b732c84..9205245 100644
--- a/java/source/conf.py
+++ b/java/source/conf.py
@@ -38,9 +38,9 @@
author = 'The Apache Software Foundation'
arrow_nightly=os.getenv("ARROW_NIGHTLY")
if arrow_nightly and arrow_nightly != '0':
- version = "16.0.0-SNAPSHOT"
+ version = "17.0.0-SNAPSHOT"
else:
- version = "15.0.2"
+ version = "16.0.0"
print(f"Running with Arrow version: {version}")
# -- General configuration ---------------------------------------------------
diff --git a/java/source/demo/pom.xml b/java/source/demo/pom.xml
index 02a5d8c..019f543 100644
--- a/java/source/demo/pom.xml
+++ b/java/source/demo/pom.xml
@@ -41,7 +41,7 @@
<properties>
<maven.compiler.source>11</maven.compiler.source>
<maven.compiler.target>11</maven.compiler.target>
- <arrow.version>15.0.2</arrow.version>
+ <arrow.version>16.0.0</arrow.version>
</properties>
<dependencies>
<dependency>
diff --git a/python/requirements.txt b/python/requirements.txt
index 81136fb..1b5fd6a 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -1,5 +1,5 @@
Sphinx>=4.0.2
-pyarrow==15.0.2
+pyarrow==16.0.0
pandas>=1.2.5
opentelemetry-api>=1.0.0
opentelemetry-sdk>=1.0.0
\ No newline at end of file
diff --git a/r/content/specify_data_types_and_schemas.Rmd b/r/content/specify_data_types_and_schemas.Rmd
index 1a18085..f87012a 100644
--- a/r/content/specify_data_types_and_schemas.Rmd
+++ b/r/content/specify_data_types_and_schemas.Rmd
@@ -21,19 +21,19 @@
## Introduction
-As discussed in previous chapters, Arrow automatically infers the most
-appropriate data type when reading in data or converting R objects to Arrow
-objects. However, you might want to manually tell Arrow which data types to
-use, for example, to ensure interoperability with databases and data warehouse
+As discussed in previous chapters, Arrow automatically infers the most
+appropriate data type when reading in data or converting R objects to Arrow
+objects. However, you might want to manually tell Arrow which data types to
+use, for example, to ensure interoperability with databases and data warehouse
systems. This chapter includes recipes for:
* changing the data types of existing Arrow objects
* defining data types during the process of creating Arrow objects
-A table showing the default mappings between R and Arrow data types can be found
+A table showing the default mappings between R and Arrow data types can be found
in [R data type to Arrow data type mappings](https://arrow.apache.org/docs/r/articles/arrow.html#r-to-arrow).
-A table containing Arrow data types, and their R equivalents can be found in
+A table containing Arrow data types, and their R equivalents can be found in
[Arrow data type to R data type mapping](https://arrow.apache.org/docs/r/articles/arrow.html#arrow-to-r).
## Update data type of an existing Arrow Array
@@ -63,7 +63,7 @@
### Discussion
-There are some data types which are not compatible with each other. Errors will
+There are some data types which are not compatible with each other. Errors will
occur if you try to cast between incompatible data types.
```{r, incompat, eval = FALSE}
@@ -120,48 +120,9 @@
})
```
-### Discussion {#no-compat-type}
-
-There are some Arrow data types which do not have any R equivalent. Attempting
-to cast to these data types or using a schema which contains them will result in
-an error.
-
-```{r, float_16_conversion, error=TRUE, eval=FALSE}
-# Set up a tibble to use in this example
-oscars <- tibble::tibble(
- actor = c("Katharine Hepburn", "Meryl Streep", "Jack Nicholson"),
- num_awards = c(4, 3, 3)
-)
-
-# Convert tibble to an Arrow table
-oscars_arrow <- arrow_table(oscars)
-
-# Set up schema with "num_awards" as float16 which doesn't have an R equivalent
-oscars_schema_invalid <- schema(actor = string(), num_awards = float16())
-
-# The default mapping from numeric column "num_awards" is to a double
-oscars_arrow$cast(target_schema = oscars_schema_invalid)
-```
-
-```{r}
-## Error: NotImplemented: Unsupported cast from double to halffloat using function cast_half_float
-```
-
-```{r, test_float_16_conversion, opts.label = "test"}
-test_that("float_16_conversion works as expected", {
-
- oscars_schema_invalid <- schema(actor = string(), num_awards = float16())
-
- expect_error(
- oscars_arrow$cast(target_schema = oscars_schema_invalid),
- "NotImplemented: Unsupported cast from double to halffloat using function cast_half_float"
- )
-})
-```
-
## Specify data types when creating an Arrow table from an R object
-You want to manually specify Arrow data types when converting an object from a
+You want to manually specify Arrow data types when converting an object from a
data frame to an Arrow object.
### Solution
@@ -226,4 +187,3 @@
```{r, include=FALSE}
unlink("oscars_data", recursive = TRUE)
```
-
diff --git a/r/content/tables.Rmd b/r/content/tables.Rmd
index 89225ab..ce06767 100644
--- a/r/content/tables.Rmd
+++ b/r/content/tables.Rmd
@@ -191,9 +191,7 @@
arrow_table(starwars) %>%
mutate(name_split = str_split_fixed(name, " ", 2)) %>%
collect(),
- 'Expression str_split_fixed(name, " ", 2) not supported in Arrow; pulling data into R',
- fixed = TRUE
- )
+ 'In str_split_fixed\\(name, " ", 2\\):.*Expression not supported in Arrow.*Pulling data into R')
})
```
@@ -415,4 +413,4 @@
- Computing the aggregation separately, and joining the result
- Passing the data to DuckDB, and use the DuckDB query engine to perform the operations
-Arrow supports zero-copy integration with DuckDB, and DuckDB can query Arrow datasets directly and stream query results back to Arrow. This integreation uses zero-copy streaming of data between DuckDB and Arrow and vice versa so that you can compose a query using both together, all the while not paying any cost to (re)serialize the data when you pass it back and forth. This is especially useful in cases where something is supported in one of Arrow or DuckDB query engines but not the other. You can find more information about this integration on the [Arrow blog post](https://arrow.apache.org/blog/2021/12/03/arrow-duckdb/).
\ No newline at end of file
+Arrow supports zero-copy integration with DuckDB, and DuckDB can query Arrow datasets directly and stream query results back to Arrow. This integreation uses zero-copy streaming of data between DuckDB and Arrow and vice versa so that you can compose a query using both together, all the while not paying any cost to (re)serialize the data when you pass it back and forth. This is especially useful in cases where something is supported in one of Arrow or DuckDB query engines but not the other. You can find more information about this integration on the [Arrow blog post](https://arrow.apache.org/blog/2021/12/03/arrow-duckdb/).