[C++] unify_schemas can't handle int64 + double, affects CSV dataset #30245

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[C++] unify_schemas can't handle int64 + double, affects CSV dataset#30245
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Component: C++Component: PythonComponent: RType: bug

Description

@asfimport
asfimport
opened on Nov 12, 2021

Twitter question of "how can I make arrow's csv reader not make int64 for integers", turns out to be originating from the scenario where some csvs in a directory may have all integer values for a column but there are decimals in others, and you can't use them together in a dataset.

library(arrow, warn.conflicts=FALSE)
library(dplyr, warn.conflicts=FALSE)
ds_dir<- tempfile()
dir.create(ds_dir)
cat("a\n1", file= file.path(ds_dir, "1.csv"))
cat("a\n1.1", file= file.path(ds_dir, "2.csv"))
ds<- open_dataset(ds_dir, format="csv")
ds#> FileSystemDataset with 2 csv files#> a: int64## It just picked the schema of the first file
collect(ds)
#> Error: Invalid: Could not open CSV input source '/private/var/folders/yv/b6mwztyj0r11r8pnsbmpltx00000gn/T/RtmpzENOMb/filea9c3292e06dd/2.csv': Invalid: In CSV column #0: Row #2: CSV conversion error to int64: invalid value '1.1'#> ../src/arrow/csv/converter.cc:492 decoder_.Decode(data, size, quoted, &value)#> ../src/arrow/csv/parser.h:123 status#> ../src/arrow/csv/converter.cc:496 parser.VisitColumn(col_index, visit)#> ../src/arrow/csv/reader.cc:462 internal::UnwrapOrRaise(maybe_decoded_arrays)#> ../src/arrow/compute/exec/exec_plan.cc:398 iterator_.Next()#> ../src/arrow/record_batch.cc:318 ReadNext(&batch)#> ../src/arrow/record_batch.cc:329 ReadAll(&batches)## Let's try again and tell it to unify schemas. Should result in a float64 typeds<- open_dataset(ds_dir, format="csv", unify_schemas=TRUE)
#> Error: Invalid: Unable to merge: Field a has incompatible types: int64 vs double#> ../src/arrow/type.cc:1621 fields_[i]->MergeWith(field)#> ../src/arrow/type.cc:1684 AddField(field)#> ../src/arrow/type.cc:1755 builder.AddSchema(schema)#> ../src/arrow/dataset/discovery.cc:251 Inspect(options.inspect_options)

Reporter: Neal Richardson / @nealrichardson

Related issues:

PRs and other links:

Note: This issue was originally created as ARROW-14705. Please see the migration documentation for further details.

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      , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

      Skip to content
      [C++] unify_schemas can't handle int64 + double, affects CSV dataset #30245
      Open
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      [C++] unify_schemas can't handle int64 + double, affects CSV dataset#30245
      Labels
      Component: C++Component: PythonComponent: RType: bug
      Description
      @asfimport
      asfimport
      opened on Nov 12, 2021
      Twitter question of "how can I make arrow's csv reader not make int64 for integers", turns out to be originating from the scenario where some csvs in a directory may have all integer values for a column but there are decimals in others, and you can't use them together in a dataset.
      library(arrow, warn.conflicts=FALSE)
library(dplyr, warn.conflicts=FALSE)
ds_dir<- tempfile()
dir.create(ds_dir)
cat("a\n1", file= file.path(ds_dir, "1.csv"))
cat("a\n1.1", file= file.path(ds_dir, "2.csv"))
ds<- open_dataset(ds_dir, format="csv")
ds#> FileSystemDataset with 2 csv files#> a: int64## It just picked the schema of the first file
collect(ds)
#> Error: Invalid: Could not open CSV input source '/private/var/folders/yv/b6mwztyj0r11r8pnsbmpltx00000gn/T/RtmpzENOMb/filea9c3292e06dd/2.csv': Invalid: In CSV column #0: Row #2: CSV conversion error to int64: invalid value '1.1'#> ../src/arrow/csv/converter.cc:492 decoder_.Decode(data, size, quoted, &value)#> ../src/arrow/csv/parser.h:123 status#> ../src/arrow/csv/converter.cc:496 parser.VisitColumn(col_index, visit)#> ../src/arrow/csv/reader.cc:462 internal::UnwrapOrRaise(maybe_decoded_arrays)#> ../src/arrow/compute/exec/exec_plan.cc:398 iterator_.Next()#> ../src/arrow/record_batch.cc:318 ReadNext(&batch)#> ../src/arrow/record_batch.cc:329 ReadAll(&batches)## Let's try again and tell it to unify schemas. Should result in a float64 typeds<- open_dataset(ds_dir, format="csv", unify_schemas=TRUE)
#> Error: Invalid: Unable to merge: Field a has incompatible types: int64 vs double#> ../src/arrow/type.cc:1621 fields_[i]->MergeWith(field)#> ../src/arrow/type.cc:1684 AddField(field)#> ../src/arrow/type.cc:1755 builder.AddSchema(schema)#> ../src/arrow/dataset/discovery.cc:251 Inspect(options.inspect_options)
      Reporter: Neal Richardson / @nealrichardson
      Related issues:
      PRs and other links:
      Note: This issue was originally created as ARROW-14705. Please see the migration documentation for further details.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

          Skip to content
          [C++] unify_schemas can't handle int64 + double, affects CSV dataset #30245
          Open
          Open
          [C++] unify_schemas can't handle int64 + double, affects CSV dataset#30245
          Labels
          Component: C++Component: PythonComponent: RType: bug
          Description
          @asfimport
          asfimport
          opened on Nov 12, 2021
          Twitter question of "how can I make arrow's csv reader not make int64 for integers", turns out to be originating from the scenario where some csvs in a directory may have all integer values for a column but there are decimals in others, and you can't use them together in a dataset.
          library(arrow, warn.conflicts=FALSE)
library(dplyr, warn.conflicts=FALSE)
ds_dir<- tempfile()
dir.create(ds_dir)
cat("a\n1", file= file.path(ds_dir, "1.csv"))
cat("a\n1.1", file= file.path(ds_dir, "2.csv"))
ds<- open_dataset(ds_dir, format="csv")
ds#> FileSystemDataset with 2 csv files#> a: int64## It just picked the schema of the first file
collect(ds)
#> Error: Invalid: Could not open CSV input source '/private/var/folders/yv/b6mwztyj0r11r8pnsbmpltx00000gn/T/RtmpzENOMb/filea9c3292e06dd/2.csv': Invalid: In CSV column #0: Row #2: CSV conversion error to int64: invalid value '1.1'#> ../src/arrow/csv/converter.cc:492 decoder_.Decode(data, size, quoted, &value)#> ../src/arrow/csv/parser.h:123 status#> ../src/arrow/csv/converter.cc:496 parser.VisitColumn(col_index, visit)#> ../src/arrow/csv/reader.cc:462 internal::UnwrapOrRaise(maybe_decoded_arrays)#> ../src/arrow/compute/exec/exec_plan.cc:398 iterator_.Next()#> ../src/arrow/record_batch.cc:318 ReadNext(&batch)#> ../src/arrow/record_batch.cc:329 ReadAll(&batches)## Let's try again and tell it to unify schemas. Should result in a float64 typeds<- open_dataset(ds_dir, format="csv", unify_schemas=TRUE)
#> Error: Invalid: Unable to merge: Field a has incompatible types: int64 vs double#> ../src/arrow/type.cc:1621 fields_[i]->MergeWith(field)#> ../src/arrow/type.cc:1684 AddField(field)#> ../src/arrow/type.cc:1755 builder.AddSchema(schema)#> ../src/arrow/dataset/discovery.cc:251 Inspect(options.inspect_options)
          Reporter: Neal Richardson / @nealrichardson
          Related issues:
          PRs and other links:
          Note: This issue was originally created as ARROW-14705. Please see the migration documentation for further details.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

              Skip to content
              [C++] unify_schemas can't handle int64 + double, affects CSV dataset #30245
              Open
              Open
              [C++] unify_schemas can't handle int64 + double, affects CSV dataset#30245
              Labels
              Component: C++Component: PythonComponent: RType: bug
              Description
              @asfimport
              asfimport
              opened on Nov 12, 2021
              Twitter question of "how can I make arrow's csv reader not make int64 for integers", turns out to be originating from the scenario where some csvs in a directory may have all integer values for a column but there are decimals in others, and you can't use them together in a dataset.
              library(arrow, warn.conflicts=FALSE)
library(dplyr, warn.conflicts=FALSE)
ds_dir<- tempfile()
dir.create(ds_dir)
cat("a\n1", file= file.path(ds_dir, "1.csv"))
cat("a\n1.1", file= file.path(ds_dir, "2.csv"))
ds<- open_dataset(ds_dir, format="csv")
ds#> FileSystemDataset with 2 csv files#> a: int64## It just picked the schema of the first file
collect(ds)
#> Error: Invalid: Could not open CSV input source '/private/var/folders/yv/b6mwztyj0r11r8pnsbmpltx00000gn/T/RtmpzENOMb/filea9c3292e06dd/2.csv': Invalid: In CSV column #0: Row #2: CSV conversion error to int64: invalid value '1.1'#> ../src/arrow/csv/converter.cc:492 decoder_.Decode(data, size, quoted, &value)#> ../src/arrow/csv/parser.h:123 status#> ../src/arrow/csv/converter.cc:496 parser.VisitColumn(col_index, visit)#> ../src/arrow/csv/reader.cc:462 internal::UnwrapOrRaise(maybe_decoded_arrays)#> ../src/arrow/compute/exec/exec_plan.cc:398 iterator_.Next()#> ../src/arrow/record_batch.cc:318 ReadNext(&batch)#> ../src/arrow/record_batch.cc:329 ReadAll(&batches)## Let's try again and tell it to unify schemas. Should result in a float64 typeds<- open_dataset(ds_dir, format="csv", unify_schemas=TRUE)
#> Error: Invalid: Unable to merge: Field a has incompatible types: int64 vs double#> ../src/arrow/type.cc:1621 fields_[i]->MergeWith(field)#> ../src/arrow/type.cc:1684 AddField(field)#> ../src/arrow/type.cc:1755 builder.AddSchema(schema)#> ../src/arrow/dataset/discovery.cc:251 Inspect(options.inspect_options)
              Reporter: Neal Richardson / @nealrichardson
              Related issues:
              PRs and other links:
              Note: This issue was originally created as ARROW-14705. Please see the migration documentation for further details.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

                  Skip to content
                  [C++] unify_schemas can't handle int64 + double, affects CSV dataset #30245
                  Open
                  Open
                  [C++] unify_schemas can't handle int64 + double, affects CSV dataset#30245
                  Labels
                  Component: C++Component: PythonComponent: RType: bug
                  Description
                  @asfimport
                  asfimport
                  opened on Nov 12, 2021
                  Twitter question of "how can I make arrow's csv reader not make int64 for integers", turns out to be originating from the scenario where some csvs in a directory may have all integer values for a column but there are decimals in others, and you can't use them together in a dataset.
                  library(arrow, warn.conflicts=FALSE)
library(dplyr, warn.conflicts=FALSE)
ds_dir<- tempfile()
dir.create(ds_dir)
cat("a\n1", file= file.path(ds_dir, "1.csv"))
cat("a\n1.1", file= file.path(ds_dir, "2.csv"))
ds<- open_dataset(ds_dir, format="csv")
ds#> FileSystemDataset with 2 csv files#> a: int64## It just picked the schema of the first file
collect(ds)
#> Error: Invalid: Could not open CSV input source '/private/var/folders/yv/b6mwztyj0r11r8pnsbmpltx00000gn/T/RtmpzENOMb/filea9c3292e06dd/2.csv': Invalid: In CSV column #0: Row #2: CSV conversion error to int64: invalid value '1.1'#> ../src/arrow/csv/converter.cc:492 decoder_.Decode(data, size, quoted, &value)#> ../src/arrow/csv/parser.h:123 status#> ../src/arrow/csv/converter.cc:496 parser.VisitColumn(col_index, visit)#> ../src/arrow/csv/reader.cc:462 internal::UnwrapOrRaise(maybe_decoded_arrays)#> ../src/arrow/compute/exec/exec_plan.cc:398 iterator_.Next()#> ../src/arrow/record_batch.cc:318 ReadNext(&batch)#> ../src/arrow/record_batch.cc:329 ReadAll(&batches)## Let's try again and tell it to unify schemas. Should result in a float64 typeds<- open_dataset(ds_dir, format="csv", unify_schemas=TRUE)
#> Error: Invalid: Unable to merge: Field a has incompatible types: int64 vs double#> ../src/arrow/type.cc:1621 fields_[i]->MergeWith(field)#> ../src/arrow/type.cc:1684 AddField(field)#> ../src/arrow/type.cc:1755 builder.AddSchema(schema)#> ../src/arrow/dataset/discovery.cc:251 Inspect(options.inspect_options)
                  Reporter: Neal Richardson / @nealrichardson
                  Related issues:
                  PRs and other links:
                  Note: This issue was originally created as ARROW-14705. Please see the migration documentation for further details.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

                      Skip to content
                      [C++] unify_schemas can't handle int64 + double, affects CSV dataset #30245
                      Open
                      Open
                      [C++] unify_schemas can't handle int64 + double, affects CSV dataset#30245
                      Labels
                      Component: C++Component: PythonComponent: RType: bug
                      Description
                      @asfimport
                      asfimport
                      opened on Nov 12, 2021
                      Twitter question of "how can I make arrow's csv reader not make int64 for integers", turns out to be originating from the scenario where some csvs in a directory may have all integer values for a column but there are decimals in others, and you can't use them together in a dataset.
                      library(arrow, warn.conflicts=FALSE)
library(dplyr, warn.conflicts=FALSE)
ds_dir<- tempfile()
dir.create(ds_dir)
cat("a\n1", file= file.path(ds_dir, "1.csv"))
cat("a\n1.1", file= file.path(ds_dir, "2.csv"))
ds<- open_dataset(ds_dir, format="csv")
ds#> FileSystemDataset with 2 csv files#> a: int64## It just picked the schema of the first file
collect(ds)
#> Error: Invalid: Could not open CSV input source '/private/var/folders/yv/b6mwztyj0r11r8pnsbmpltx00000gn/T/RtmpzENOMb/filea9c3292e06dd/2.csv': Invalid: In CSV column #0: Row #2: CSV conversion error to int64: invalid value '1.1'#> ../src/arrow/csv/converter.cc:492 decoder_.Decode(data, size, quoted, &value)#> ../src/arrow/csv/parser.h:123 status#> ../src/arrow/csv/converter.cc:496 parser.VisitColumn(col_index, visit)#> ../src/arrow/csv/reader.cc:462 internal::UnwrapOrRaise(maybe_decoded_arrays)#> ../src/arrow/compute/exec/exec_plan.cc:398 iterator_.Next()#> ../src/arrow/record_batch.cc:318 ReadNext(&batch)#> ../src/arrow/record_batch.cc:329 ReadAll(&batches)## Let's try again and tell it to unify schemas. Should result in a float64 typeds<- open_dataset(ds_dir, format="csv", unify_schemas=TRUE)
#> Error: Invalid: Unable to merge: Field a has incompatible types: int64 vs double#> ../src/arrow/type.cc:1621 fields_[i]->MergeWith(field)#> ../src/arrow/type.cc:1684 AddField(field)#> ../src/arrow/type.cc:1755 builder.AddSchema(schema)#> ../src/arrow/dataset/discovery.cc:251 Inspect(options.inspect_options)
                      Reporter: Neal Richardson / @nealrichardson
                      Related issues:
                      PRs and other links:
                      Note: This issue was originally created as ARROW-14705. Please see the migration documentation for further details.
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                      No one assigned
                        Labels
                        Component: C++Component: PythonComponent: RType: bug
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                          No branches or pull requests
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

                          Skip to content
                          [C++] unify_schemas can't handle int64 + double, affects CSV dataset #30245
                          Open
                          Open
                          [C++] unify_schemas can't handle int64 + double, affects CSV dataset#30245
                          Labels
                          Component: C++Component: PythonComponent: RType: bug
                          Description
                          @asfimport
                          asfimport
                          opened on Nov 12, 2021
                          Twitter question of "how can I make arrow's csv reader not make int64 for integers", turns out to be originating from the scenario where some csvs in a directory may have all integer values for a column but there are decimals in others, and you can't use them together in a dataset.
                          library(arrow, warn.conflicts=FALSE)
library(dplyr, warn.conflicts=FALSE)
ds_dir<- tempfile()
dir.create(ds_dir)
cat("a\n1", file= file.path(ds_dir, "1.csv"))
cat("a\n1.1", file= file.path(ds_dir, "2.csv"))
ds<- open_dataset(ds_dir, format="csv")
ds#> FileSystemDataset with 2 csv files#> a: int64## It just picked the schema of the first file
collect(ds)
#> Error: Invalid: Could not open CSV input source '/private/var/folders/yv/b6mwztyj0r11r8pnsbmpltx00000gn/T/RtmpzENOMb/filea9c3292e06dd/2.csv': Invalid: In CSV column #0: Row #2: CSV conversion error to int64: invalid value '1.1'#> ../src/arrow/csv/converter.cc:492 decoder_.Decode(data, size, quoted, &value)#> ../src/arrow/csv/parser.h:123 status#> ../src/arrow/csv/converter.cc:496 parser.VisitColumn(col_index, visit)#> ../src/arrow/csv/reader.cc:462 internal::UnwrapOrRaise(maybe_decoded_arrays)#> ../src/arrow/compute/exec/exec_plan.cc:398 iterator_.Next()#> ../src/arrow/record_batch.cc:318 ReadNext(&batch)#> ../src/arrow/record_batch.cc:329 ReadAll(&batches)## Let's try again and tell it to unify schemas. Should result in a float64 typeds<- open_dataset(ds_dir, format="csv", unify_schemas=TRUE)
#> Error: Invalid: Unable to merge: Field a has incompatible types: int64 vs double#> ../src/arrow/type.cc:1621 fields_[i]->MergeWith(field)#> ../src/arrow/type.cc:1684 AddField(field)#> ../src/arrow/type.cc:1755 builder.AddSchema(schema)#> ../src/arrow/dataset/discovery.cc:251 Inspect(options.inspect_options)
                          Reporter: Neal Richardson / @nealrichardson
                          Related issues:
                          PRs and other links:
                          Note: This issue was originally created as ARROW-14705. Please see the migration documentation for further details.
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                              [C++] unify_schemas can't handle int64 + double, affects CSV dataset #30245
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                              [C++] unify_schemas can't handle int64 + double, affects CSV dataset#30245
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                              Component: C++Component: PythonComponent: RType: bug
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                              asfimport
                              opened on Nov 12, 2021
                              Twitter question of "how can I make arrow's csv reader not make int64 for integers", turns out to be originating from the scenario where some csvs in a directory may have all integer values for a column but there are decimals in others, and you can't use them together in a dataset.
                              library(arrow, warn.conflicts=FALSE)
library(dplyr, warn.conflicts=FALSE)
ds_dir<- tempfile()
dir.create(ds_dir)
cat("a\n1", file= file.path(ds_dir, "1.csv"))
cat("a\n1.1", file= file.path(ds_dir, "2.csv"))
ds<- open_dataset(ds_dir, format="csv")
ds#> FileSystemDataset with 2 csv files#> a: int64## It just picked the schema of the first file
collect(ds)
#> Error: Invalid: Could not open CSV input source '/private/var/folders/yv/b6mwztyj0r11r8pnsbmpltx00000gn/T/RtmpzENOMb/filea9c3292e06dd/2.csv': Invalid: In CSV column #0: Row #2: CSV conversion error to int64: invalid value '1.1'#> ../src/arrow/csv/converter.cc:492 decoder_.Decode(data, size, quoted, &value)#> ../src/arrow/csv/parser.h:123 status#> ../src/arrow/csv/converter.cc:496 parser.VisitColumn(col_index, visit)#> ../src/arrow/csv/reader.cc:462 internal::UnwrapOrRaise(maybe_decoded_arrays)#> ../src/arrow/compute/exec/exec_plan.cc:398 iterator_.Next()#> ../src/arrow/record_batch.cc:318 ReadNext(&batch)#> ../src/arrow/record_batch.cc:329 ReadAll(&batches)## Let's try again and tell it to unify schemas. Should result in a float64 typeds<- open_dataset(ds_dir, format="csv", unify_schemas=TRUE)
#> Error: Invalid: Unable to merge: Field a has incompatible types: int64 vs double#> ../src/arrow/type.cc:1621 fields_[i]->MergeWith(field)#> ../src/arrow/type.cc:1684 AddField(field)#> ../src/arrow/type.cc:1755 builder.AddSchema(schema)#> ../src/arrow/dataset/discovery.cc:251 Inspect(options.inspect_options)
                              Reporter: Neal Richardson / @nealrichardson
                              Related issues:
                              PRs and other links:
                              Note: This issue was originally created as ARROW-14705. Please see the migration documentation for further details.
                              Metadata
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                              Assignees
                              No one assigned
                                Labels
                                Component: C++Component: PythonComponent: RType: bug
                                Type
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                                Projects
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