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Data Prepper

codecov

Data Prepper

We envision Data Prepper as an open source data collector for observability data (trace, logs, metrics) that can filter, enrich, transform, normalize, and aggregate data for downstream analysis and visualization. It will support stateful processing across multiple instances of data pipelines for observability use cases such as distributed tracing and multi-line log events (e.g. stack traces, aggregations, and log-to-metric transformations). Currently Data Prepper supports processing of distributed trace data and log ingestion with plans to support metric data in the future.

Please read the Overview to understand what Data Prepper is and how it works.

Getting Started

Our Getting Started guide is the best starting point for anybody who wants to run Data Prepper.

Please read the Trace Analytics guide or Log Analytics to get started with using Data Prepper for trace or log analytics use cases.

Project Resources

Contribute

We invite developers from the larger OpenSearch community to contribute and help improve test coverage and give us feedback on where improvements can be made in design, code and documentation. You can look at contribution guide for more information on how to contribute.

If you are looking to contribute code, or just to build from source, please see our Developer Guide.

Code of Conduct

This project has adopted an Open Source Code of Conduct.

Security Issue Notifications

If you discover a potential security issue in this project, please refer to the security policy.

License

This library is licensed under the Apache 2.0 License

Copyright

Copyright OpenSearch Contributors. See NOTICE for details.

About

Data Prepper is a component of the OpenSearch project that accepts, filters, transforms, enriches, and routes data at scale.

Resources

Code of conduct

Contributing

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0 stars

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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" + '
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Data Prepper

codecov

Data Prepper

We envision Data Prepper as an open source data collector for observability data (trace, logs, metrics) that can filter, enrich, transform, normalize, and aggregate data for downstream analysis and visualization. It will support stateful processing across multiple instances of data pipelines for observability use cases such as distributed tracing and multi-line log events (e.g. stack traces, aggregations, and log-to-metric transformations). Currently Data Prepper supports processing of distributed trace data and log ingestion with plans to support metric data in the future.

Please read the Overview to understand what Data Prepper is and how it works.

Getting Started

Our Getting Started guide is the best starting point for anybody who wants to run Data Prepper.

Please read the Trace Analytics guide or Log Analytics to get started with using Data Prepper for trace or log analytics use cases.

Project Resources

Contribute

We invite developers from the larger OpenSearch community to contribute and help improve test coverage and give us feedback on where improvements can be made in design, code and documentation. You can look at contribution guide for more information on how to contribute.

If you are looking to contribute code, or just to build from source, please see our Developer Guide.

Code of Conduct

This project has adopted an Open Source Code of Conduct.

Security Issue Notifications

If you discover a potential security issue in this project, please refer to the security policy.

License

This library is licensed under the Apache 2.0 License

Copyright

Copyright OpenSearch Contributors. See NOTICE for details.

About

Data Prepper is a component of the OpenSearch project that accepts, filters, transforms, enriches, and routes data at scale.

Resources

Code of conduct

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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('^' + ".*" + '
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Data Prepper

codecov

Data Prepper

We envision Data Prepper as an open source data collector for observability data (trace, logs, metrics) that can filter, enrich, transform, normalize, and aggregate data for downstream analysis and visualization. It will support stateful processing across multiple instances of data pipelines for observability use cases such as distributed tracing and multi-line log events (e.g. stack traces, aggregations, and log-to-metric transformations). Currently Data Prepper supports processing of distributed trace data and log ingestion with plans to support metric data in the future.

Please read the Overview to understand what Data Prepper is and how it works.

Getting Started

Our Getting Started guide is the best starting point for anybody who wants to run Data Prepper.

Please read the Trace Analytics guide or Log Analytics to get started with using Data Prepper for trace or log analytics use cases.

Project Resources

Contribute

We invite developers from the larger OpenSearch community to contribute and help improve test coverage and give us feedback on where improvements can be made in design, code and documentation. You can look at contribution guide for more information on how to contribute.

If you are looking to contribute code, or just to build from source, please see our Developer Guide.

Code of Conduct

This project has adopted an Open Source Code of Conduct.

Security Issue Notifications

If you discover a potential security issue in this project, please refer to the security policy.

License

This library is licensed under the Apache 2.0 License

Copyright

Copyright OpenSearch Contributors. See NOTICE for details.

About

Data Prepper is a component of the OpenSearch project that accepts, filters, transforms, enriches, and routes data at scale.

Resources

Code of conduct

Contributing

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages

, '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('^' + ".*" + '
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Data Prepper

codecov

Data Prepper

We envision Data Prepper as an open source data collector for observability data (trace, logs, metrics) that can filter, enrich, transform, normalize, and aggregate data for downstream analysis and visualization. It will support stateful processing across multiple instances of data pipelines for observability use cases such as distributed tracing and multi-line log events (e.g. stack traces, aggregations, and log-to-metric transformations). Currently Data Prepper supports processing of distributed trace data and log ingestion with plans to support metric data in the future.

Please read the Overview to understand what Data Prepper is and how it works.

Getting Started

Our Getting Started guide is the best starting point for anybody who wants to run Data Prepper.

Please read the Trace Analytics guide or Log Analytics to get started with using Data Prepper for trace or log analytics use cases.

Project Resources

Contribute

We invite developers from the larger OpenSearch community to contribute and help improve test coverage and give us feedback on where improvements can be made in design, code and documentation. You can look at contribution guide for more information on how to contribute.

If you are looking to contribute code, or just to build from source, please see our Developer Guide.

Code of Conduct

This project has adopted an Open Source Code of Conduct.

Security Issue Notifications

If you discover a potential security issue in this project, please refer to the security policy.

License

This library is licensed under the Apache 2.0 License

Copyright

Copyright OpenSearch Contributors. See NOTICE for details.

About

Data Prepper is a component of the OpenSearch project that accepts, filters, transforms, enriches, and routes data at scale.

Resources

Code of conduct

Contributing

Stars

0 stars

Watchers

0 watching

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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" + '
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Data Prepper

codecov

Data Prepper

We envision Data Prepper as an open source data collector for observability data (trace, logs, metrics) that can filter, enrich, transform, normalize, and aggregate data for downstream analysis and visualization. It will support stateful processing across multiple instances of data pipelines for observability use cases such as distributed tracing and multi-line log events (e.g. stack traces, aggregations, and log-to-metric transformations). Currently Data Prepper supports processing of distributed trace data and log ingestion with plans to support metric data in the future.

Please read the Overview to understand what Data Prepper is and how it works.

Getting Started

Our Getting Started guide is the best starting point for anybody who wants to run Data Prepper.

Please read the Trace Analytics guide or Log Analytics to get started with using Data Prepper for trace or log analytics use cases.

Project Resources

Contribute

We invite developers from the larger OpenSearch community to contribute and help improve test coverage and give us feedback on where improvements can be made in design, code and documentation. You can look at contribution guide for more information on how to contribute.

If you are looking to contribute code, or just to build from source, please see our Developer Guide.

Code of Conduct

This project has adopted an Open Source Code of Conduct.

Security Issue Notifications

If you discover a potential security issue in this project, please refer to the security policy.

License

This library is licensed under the Apache 2.0 License

Copyright

Copyright OpenSearch Contributors. See NOTICE for details.

About

Data Prepper is a component of the OpenSearch project that accepts, filters, transforms, enriches, and routes data at scale.

Resources

Code of conduct

Contributing

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages

, '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('^' + ".*" + '
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Data Prepper

codecov

Data Prepper

We envision Data Prepper as an open source data collector for observability data (trace, logs, metrics) that can filter, enrich, transform, normalize, and aggregate data for downstream analysis and visualization. It will support stateful processing across multiple instances of data pipelines for observability use cases such as distributed tracing and multi-line log events (e.g. stack traces, aggregations, and log-to-metric transformations). Currently Data Prepper supports processing of distributed trace data and log ingestion with plans to support metric data in the future.

Please read the Overview to understand what Data Prepper is and how it works.

Getting Started

Our Getting Started guide is the best starting point for anybody who wants to run Data Prepper.

Please read the Trace Analytics guide or Log Analytics to get started with using Data Prepper for trace or log analytics use cases.

Project Resources

Contribute

We invite developers from the larger OpenSearch community to contribute and help improve test coverage and give us feedback on where improvements can be made in design, code and documentation. You can look at contribution guide for more information on how to contribute.

If you are looking to contribute code, or just to build from source, please see our Developer Guide.

Code of Conduct

This project has adopted an Open Source Code of Conduct.

Security Issue Notifications

If you discover a potential security issue in this project, please refer to the security policy.

License

This library is licensed under the Apache 2.0 License

Copyright

Copyright OpenSearch Contributors. See NOTICE for details.

About

Data Prepper is a component of the OpenSearch project that accepts, filters, transforms, enriches, and routes data at scale.

Resources

Code of conduct

Contributing

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages

, '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('^' + ".*" + '
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Data Prepper

codecov

Data Prepper

We envision Data Prepper as an open source data collector for observability data (trace, logs, metrics) that can filter, enrich, transform, normalize, and aggregate data for downstream analysis and visualization. It will support stateful processing across multiple instances of data pipelines for observability use cases such as distributed tracing and multi-line log events (e.g. stack traces, aggregations, and log-to-metric transformations). Currently Data Prepper supports processing of distributed trace data and log ingestion with plans to support metric data in the future.

Please read the Overview to understand what Data Prepper is and how it works.

Getting Started

Our Getting Started guide is the best starting point for anybody who wants to run Data Prepper.

Please read the Trace Analytics guide or Log Analytics to get started with using Data Prepper for trace or log analytics use cases.

Project Resources

Contribute

We invite developers from the larger OpenSearch community to contribute and help improve test coverage and give us feedback on where improvements can be made in design, code and documentation. You can look at contribution guide for more information on how to contribute.

If you are looking to contribute code, or just to build from source, please see our Developer Guide.

Code of Conduct

This project has adopted an Open Source Code of Conduct.

Security Issue Notifications

If you discover a potential security issue in this project, please refer to the security policy.

License

This library is licensed under the Apache 2.0 License

Copyright

Copyright OpenSearch Contributors. See NOTICE for details.

About

Data Prepper is a component of the OpenSearch project that accepts, filters, transforms, enriches, and routes data at scale.

Resources

Code of conduct

Contributing

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages

, '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); } })(); })();
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Data Prepper

codecov

Data Prepper

We envision Data Prepper as an open source data collector for observability data (trace, logs, metrics) that can filter, enrich, transform, normalize, and aggregate data for downstream analysis and visualization. It will support stateful processing across multiple instances of data pipelines for observability use cases such as distributed tracing and multi-line log events (e.g. stack traces, aggregations, and log-to-metric transformations). Currently Data Prepper supports processing of distributed trace data and log ingestion with plans to support metric data in the future.

Please read the Overview to understand what Data Prepper is and how it works.

Getting Started

Our Getting Started guide is the best starting point for anybody who wants to run Data Prepper.

Please read the Trace Analytics guide or Log Analytics to get started with using Data Prepper for trace or log analytics use cases.

Project Resources

Contribute

We invite developers from the larger OpenSearch community to contribute and help improve test coverage and give us feedback on where improvements can be made in design, code and documentation. You can look at contribution guide for more information on how to contribute.

If you are looking to contribute code, or just to build from source, please see our Developer Guide.

Code of Conduct

This project has adopted an Open Source Code of Conduct.

Security Issue Notifications

If you discover a potential security issue in this project, please refer to the security policy.

License

This library is licensed under the Apache 2.0 License

Copyright

Copyright OpenSearch Contributors. See NOTICE for details.

About

Data Prepper is a component of the OpenSearch project that accepts, filters, transforms, enriches, and routes data at scale.

Resources

Code of conduct

Contributing

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages