[Python] pyarrow.csv.read_csv hangs + eats all RAM #22212

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[Python] pyarrow.csv.read_csv hangs + eats all RAM#22212
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emkornfield
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Component: PythonType: bug
Milestone
0.14.0

Description

@asfimport
asfimport
opened on Jun 29, 2019

I have quite a sparse dataset in CSV format. A wide table that has several rows but many (32k) columns. Total size ~540K.

When I read the dataset using pyarrow.csv.read_csv it hangs, gradually eats all memory and gets killed.

More details on the conditions further. Script to run and all mentioned files are under attachments.

  1. sample_32769_cols.csv is the dataset that suffers the problem.

  2. sample_32768_cols.csv is the dataset that DOES NOT suffer and is read in under 400ms on my machine. It's the same dataset without ONE last column. That last column is no different than others and has empty values.

The reason of why exactly this column makes difference between proper execution and hanging failure which looks like some memory leak - no idea.

I have created flame graph for the case (1) to support this issue resolution (graph.svg).

Environment: Ubuntu Xenial, python 2.7
Reporter: Bogdan Klichuk
Assignee: Micah Kornfield / @emkornfield

Original Issue Attachments:

PRs and other links:

Note: This issue was originally created as ARROW-5791. 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 
     blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} 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
    [Python] pyarrow.csv.read_csv hangs + eats all RAM #22212
    Closed
    Closed
    [Python] pyarrow.csv.read_csv hangs + eats all RAM#22212
    Assignees
    emkornfield
    Labels
    Component: PythonType: bug
    Milestone
    0.14.0
    Description
    @asfimport
    asfimport
    opened on Jun 29, 2019
    I have quite a sparse dataset in CSV format. A wide table that has several rows but many (32k) columns. Total size ~540K.
    When I read the dataset using pyarrow.csv.read_csv it hangs, gradually eats all memory and gets killed.
    More details on the conditions further. Script to run and all mentioned files are under attachments.
    1. sample_32769_cols.csv is the dataset that suffers the problem.
    2. sample_32768_cols.csv is the dataset that DOES NOT suffer and is read in under 400ms on my machine. It's the same dataset without ONE last column. That last column is no different than others and has empty values.
    The reason of why exactly this column makes difference between proper execution and hanging failure which looks like some memory leak - no idea.
    I have created flame graph for the case (1) to support this issue resolution (graph.svg).
    Environment: Ubuntu Xenial, python 2.7
    Reporter: Bogdan Klichuk
    Assignee: Micah Kornfield / @emkornfield
    Original Issue Attachments:
    PRs and other links:
    Note: This issue was originally created as ARROW-5791. Please see the migration documentation for further details.
    Activity
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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
      [Python] pyarrow.csv.read_csv hangs + eats all RAM #22212
      Closed
      Closed
      [Python] pyarrow.csv.read_csv hangs + eats all RAM#22212
      Assignees
      emkornfield
      Labels
      Component: PythonType: bug
      Milestone
      0.14.0
      Description
      @asfimport
      asfimport
      opened on Jun 29, 2019
      I have quite a sparse dataset in CSV format. A wide table that has several rows but many (32k) columns. Total size ~540K.
      When I read the dataset using pyarrow.csv.read_csv it hangs, gradually eats all memory and gets killed.
      More details on the conditions further. Script to run and all mentioned files are under attachments.
      1. sample_32769_cols.csv is the dataset that suffers the problem.
      2. sample_32768_cols.csv is the dataset that DOES NOT suffer and is read in under 400ms on my machine. It's the same dataset without ONE last column. That last column is no different than others and has empty values.
      The reason of why exactly this column makes difference between proper execution and hanging failure which looks like some memory leak - no idea.
      I have created flame graph for the case (1) to support this issue resolution (graph.svg).
      Environment: Ubuntu Xenial, python 2.7
      Reporter: Bogdan Klichuk
      Assignee: Micah Kornfield / @emkornfield
      Original Issue Attachments:
      PRs and other links:
      Note: This issue was originally created as ARROW-5791. Please see the migration documentation for further details.
      Activity
      Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
      Metadata
      Metadata
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      Labels
      Component: PythonType: bug
      Type
      No type
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      No projects
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        None yet
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        No branches or pull requests
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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
        [Python] pyarrow.csv.read_csv hangs + eats all RAM #22212
        Closed
        Closed
        [Python] pyarrow.csv.read_csv hangs + eats all RAM#22212
        Assignees
        emkornfield
        Labels
        Component: PythonType: bug
        Milestone
        0.14.0
        Description
        @asfimport
        asfimport
        opened on Jun 29, 2019
        I have quite a sparse dataset in CSV format. A wide table that has several rows but many (32k) columns. Total size ~540K.
        When I read the dataset using pyarrow.csv.read_csv it hangs, gradually eats all memory and gets killed.
        More details on the conditions further. Script to run and all mentioned files are under attachments.
        1. sample_32769_cols.csv is the dataset that suffers the problem.
        2. sample_32768_cols.csv is the dataset that DOES NOT suffer and is read in under 400ms on my machine. It's the same dataset without ONE last column. That last column is no different than others and has empty values.
        The reason of why exactly this column makes difference between proper execution and hanging failure which looks like some memory leak - no idea.
        I have created flame graph for the case (1) to support this issue resolution (graph.svg).
        Environment: Ubuntu Xenial, python 2.7
        Reporter: Bogdan Klichuk
        Assignee: Micah Kornfield / @emkornfield
        Original Issue Attachments:
        PRs and other links:
        Note: This issue was originally created as ARROW-5791. Please see the migration documentation for further details.
        Activity
        Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
        Metadata
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        Component: PythonType: bug
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          None yet
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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
          [Python] pyarrow.csv.read_csv hangs + eats all RAM #22212
          Closed
          Closed
          [Python] pyarrow.csv.read_csv hangs + eats all RAM#22212
          Assignees
          emkornfield
          Labels
          Component: PythonType: bug
          Milestone
          0.14.0
          Description
          @asfimport
          asfimport
          opened on Jun 29, 2019
          I have quite a sparse dataset in CSV format. A wide table that has several rows but many (32k) columns. Total size ~540K.
          When I read the dataset using pyarrow.csv.read_csv it hangs, gradually eats all memory and gets killed.
          More details on the conditions further. Script to run and all mentioned files are under attachments.
          1. sample_32769_cols.csv is the dataset that suffers the problem.
          2. sample_32768_cols.csv is the dataset that DOES NOT suffer and is read in under 400ms on my machine. It's the same dataset without ONE last column. That last column is no different than others and has empty values.
          The reason of why exactly this column makes difference between proper execution and hanging failure which looks like some memory leak - no idea.
          I have created flame graph for the case (1) to support this issue resolution (graph.svg).
          Environment: Ubuntu Xenial, python 2.7
          Reporter: Bogdan Klichuk
          Assignee: Micah Kornfield / @emkornfield
          Original Issue Attachments:
          PRs and other links:
          Note: This issue was originally created as ARROW-5791. Please see the migration documentation for further details.
          Activity
          Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
          Metadata
          Metadata
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          Labels
          Component: PythonType: bug
          Type
          No type
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          No projects
            Milestone
            Relationships
            None yet
            Development
            No branches or pull requests
            Issue actions
            
, '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
            [Python] pyarrow.csv.read_csv hangs + eats all RAM #22212
            Closed
            Closed
            [Python] pyarrow.csv.read_csv hangs + eats all RAM#22212
            Assignees
            emkornfield
            Labels
            Component: PythonType: bug
            Milestone
            0.14.0
            Description
            @asfimport
            asfimport
            opened on Jun 29, 2019
            I have quite a sparse dataset in CSV format. A wide table that has several rows but many (32k) columns. Total size ~540K.
            When I read the dataset using pyarrow.csv.read_csv it hangs, gradually eats all memory and gets killed.
            More details on the conditions further. Script to run and all mentioned files are under attachments.
            1. sample_32769_cols.csv is the dataset that suffers the problem.
            2. sample_32768_cols.csv is the dataset that DOES NOT suffer and is read in under 400ms on my machine. It's the same dataset without ONE last column. That last column is no different than others and has empty values.
            The reason of why exactly this column makes difference between proper execution and hanging failure which looks like some memory leak - no idea.
            I have created flame graph for the case (1) to support this issue resolution (graph.svg).
            Environment: Ubuntu Xenial, python 2.7
            Reporter: Bogdan Klichuk
            Assignee: Micah Kornfield / @emkornfield
            Original Issue Attachments:
            PRs and other links:
            Note: This issue was originally created as ARROW-5791. Please see the migration documentation for further details.
            Activity
            Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
            Metadata
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            Component: PythonType: bug
            Type
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              None yet
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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
              [Python] pyarrow.csv.read_csv hangs + eats all RAM #22212
              Closed
              Closed
              [Python] pyarrow.csv.read_csv hangs + eats all RAM#22212
              Assignees
              emkornfield
              Labels
              Component: PythonType: bug
              Milestone
              0.14.0
              Description
              @asfimport
              asfimport
              opened on Jun 29, 2019
              I have quite a sparse dataset in CSV format. A wide table that has several rows but many (32k) columns. Total size ~540K.
              When I read the dataset using pyarrow.csv.read_csv it hangs, gradually eats all memory and gets killed.
              More details on the conditions further. Script to run and all mentioned files are under attachments.
              1. sample_32769_cols.csv is the dataset that suffers the problem.
              2. sample_32768_cols.csv is the dataset that DOES NOT suffer and is read in under 400ms on my machine. It's the same dataset without ONE last column. That last column is no different than others and has empty values.
              The reason of why exactly this column makes difference between proper execution and hanging failure which looks like some memory leak - no idea.
              I have created flame graph for the case (1) to support this issue resolution (graph.svg).
              Environment: Ubuntu Xenial, python 2.7
              Reporter: Bogdan Klichuk
              Assignee: Micah Kornfield / @emkornfield
              Original Issue Attachments:
              PRs and other links:
              Note: This issue was originally created as ARROW-5791. Please see the migration documentation for further details.
              Activity
              Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
              Metadata
              Metadata
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              Labels
              Component: PythonType: bug
              Type
              No type
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                None yet
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                No branches or pull requests
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

                Skip to content
                [Python] pyarrow.csv.read_csv hangs + eats all RAM #22212
                Closed
                Closed
                [Python] pyarrow.csv.read_csv hangs + eats all RAM#22212
                Assignees
                emkornfield
                Labels
                Component: PythonType: bug
                Milestone
                0.14.0
                Description
                @asfimport
                asfimport
                opened on Jun 29, 2019
                I have quite a sparse dataset in CSV format. A wide table that has several rows but many (32k) columns. Total size ~540K.
                When I read the dataset using pyarrow.csv.read_csv it hangs, gradually eats all memory and gets killed.
                More details on the conditions further. Script to run and all mentioned files are under attachments.
                1. sample_32769_cols.csv is the dataset that suffers the problem.
                2. sample_32768_cols.csv is the dataset that DOES NOT suffer and is read in under 400ms on my machine. It's the same dataset without ONE last column. That last column is no different than others and has empty values.
                The reason of why exactly this column makes difference between proper execution and hanging failure which looks like some memory leak - no idea.
                I have created flame graph for the case (1) to support this issue resolution (graph.svg).
                Environment: Ubuntu Xenial, python 2.7
                Reporter: Bogdan Klichuk
                Assignee: Micah Kornfield / @emkornfield
                Original Issue Attachments:
                PRs and other links:
                Note: This issue was originally created as ARROW-5791. Please see the migration documentation for further details.
                Activity
                Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
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                Component: PythonType: bug
                Type
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                  None yet
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                  Issue actions