Email: Postgres at 118M QPS 🏎️, ChatGPT Work Data Agent 🗣️, Lyft’s Travel-Time Map 🚕

Subject:
Postgres at 118M QPS 🏎️, ChatGPT Work Data Agent 🗣️, Lyft’s Travel-Time Map 🚕
Received:
9/14/2026, 10:12:15 AM
From:
"TLDR Data" <dan@tldrnewsletter.com>
To:
silk.theater.56@fwdnl.com
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&lt;div style="display: none; max-height: 0px; overflow: hidden;"&gt;Coding agents now produce competent PostgreSQL schemas, but they tend to stack indexes onto hot operational tables. In a measured support&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;&lt;/div&gt;

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&lt;h1&gt;&lt;strong&gt;TLDR Data &lt;span id="date"&gt;2026-09-14&lt;/span&gt;&lt;/strong&gt;&lt;/h1&gt;
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                                        &lt;strong&gt;Branch your production database like you branch your code (Sponsor)&lt;/strong&gt;
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&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    What if Postgres compute was stateless? Imagine a branch that is a metadata operation that runs in seconds, not hours. What if you could also run transactions and analytics on the same copy of data in real time without delays or CDC? One architecture, two problems solved.&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.databricks.com%2Fblog%2Flakebase-ltap-rethinking-database-storage%3Fscid=701Vp000004h4j2IAA%26dclid=COCw5Zms55YDFcVRnQkdeGgbRw%26gad_source=7%26gad_campaignid=24081725436%26utm_source=tldr%26utm_medium=thirdparty%26utm_campaign=%257Bcampaignid%257D%26utm_content=blog_cta_unlock_unlimited_storage%26utm_ad=%257Bcreative%257D%26utm_adgroup=%257Badgroupid%257D%26utm_offer=lakebase-ltap-rethinking-database-storage%26utm_term=%257Bkeyword%257D/1/010001a09f6732c2-b6d784ad-1e98-4f01-b696-8f450cb5c60d-000000/BPeRQoWokLPVUkF65jKax5p5UnOQ6p7qzlTZWkPA-EU=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;Unlock unlimited storage, elastic compute, durable writes and instant branching&lt;/span&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.youtube.com%2Fwatch%3Fv=19nQxHcWDg0%26utm_source=TLDR%26utm_medium=newsletter%26utm_campaign=2026-09-14_primary_databricks%26utm_content=cta_run_analytics_against/1/010001a09f6732c2-b6d784ad-1e98-4f01-b696-8f450cb5c60d-000000/cw7Xzy-ch2FyR6zFoNtXSgcwWAJAzbfK9AXbMQi1ctQ=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;Run analytics against the freshest transactional data&lt;/span&gt;&lt;/a&gt;


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&lt;h1&gt;&lt;strong&gt;Deep Dives&lt;/strong&gt;&lt;/h1&gt;
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                                        &lt;strong&gt;Refreshing the Travel-Time Map Behind Lyft's Marketplace: Rebuilding Neighborhood Reachability Signals (12 minute read)&lt;/strong&gt;
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                                    Lyft rebuilt its outdated neighborhood travel-time dataset with more accurate ETAs, better coverage, drivable-location filtering, and self-service fields, improving how pricing and driver heatmaps understand nearby supply. It will now refresh every six months, with time-aware ETAs planned to reflect changing traffic throughout the week.
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                                    Diskless Kafka keeps the Kafka API while moving durable payload storage from broker-owned logs to object storage and strongly consistent metadata. The tradeoff is not just cheaper retention: batching, offset assignment, recovery, compaction, latency, and feature support all change, making diskless topics a per-workload storage choice rather than a universal Kafka replacement.
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&lt;h1&gt;&lt;strong&gt;Opinions &amp; Advice&lt;/strong&gt;&lt;/h1&gt;
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                                        &lt;strong&gt;The unbearable lightness of one more index (11 minute read)&lt;/strong&gt;
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&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Coding agents now produce competent PostgreSQL schemas, but they tend to stack indexes onto hot operational tables. In a measured support-ticket workload, extra indexes nearly doubled update time and WAL while increasing VACUUM work. Index review needs write-path evidence, HOT-update awareness, and usage counters, not just fast read plans.
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&lt;/div&gt;
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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fthenewstack.io%2Fai-agent-trace-debugging%2F%3Futm_source=tldrdata/1/010001a09f6732c2-b6d784ad-1e98-4f01-b696-8f450cb5c60d-000000/W2gPG-x5Qy-TkVf1_uDGpWMkjs0wKOXBwD0Emz2Fzhg=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;It passed CI. It passed your evals. The customer still got the wrong answer (11 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    AI agents can return successful, well-grounded responses that are still wrong because they retrieved invalid context or followed a faulty execution path. Debugging requires end-to-end traces of model calls, tool calls, retrieval inputs, and outputs, plus separate tests for deterministic system behavior and answer quality.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;💻&lt;/span&gt;&lt;/div&gt;
&lt;/div&gt;
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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;

&lt;h1&gt;&lt;strong&gt;Launches &amp; Tools&lt;/strong&gt;&lt;/h1&gt;
&lt;/div&gt;
&lt;/div&gt;
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&lt;table style="table-layout: fixed; width: 100%;" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td style="padding:0;border-collapse:collapse;border-spacing:0;margin:0;" valign="top"&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fchalk.ai%2Fblog%2Fwhat-is-a-context-engine%3Futm_source=tldr%26utm_medium=referral%26utm_campaign=2026-09-14_contextengineblog%26utm_term=tldrdata/1/010001a09f6732c2-b6d784ad-1e98-4f01-b696-8f450cb5c60d-000000/jXYYL30aN4hiCemafnU1Rb_-Jg0AYstKzlzCyq1iwFQ=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Stale data makes agents fail confidently (Sponsor)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Most AI agents fail confidently, not loudly. They act on stale data and produce wrong refunds, outdated quotes, and contradictory decisions. A &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fchalk.ai%2Fblog%2Fwhat-is-a-context-engine%3Futm_source=tldr%26utm_medium=referral%26utm_campaign=2026-09-14_contextengineblog%26utm_term=tldrdata/2/010001a09f6732c2-b6d784ad-1e98-4f01-b696-8f450cb5c60d-000000/cHMGPruAfkLMxueVzC-t4WdiISVn96YQhKIg1Hvidws=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;context engine&lt;/span&gt;&lt;/a&gt; computes real-time values at the moment of each decision, so your agents act on current, accurate context. &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fchalk.ai%2Fblog%2Fwhat-is-a-context-engine%3Futm_source=tldr%26utm_medium=referral%26utm_campaign=2026-09-14_contextengineblog%26utm_term=tldrdata/3/010001a09f6732c2-b6d784ad-1e98-4f01-b696-8f450cb5c60d-000000/LQVyEertltFCAapmbxS0ARrp8RAWqrZBFLSO6trApYY=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;See exactly how it works.&lt;/span&gt;&lt;/a&gt;
&lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fplanetscale.com%2Fblog%2F118-million-queries-per-second-on-neki%3Futm_source=tldrdata/1/010001a09f6732c2-b6d784ad-1e98-4f01-b696-8f450cb5c60d-000000/PHZmQXrojghmUuYU_xSJAbps0KJX6DKfP4QO11Pvb_0=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;118 million queries per second on Neki (9 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    PlanetScale set out to run 1 million queries per second against Neki, its new sharded Postgres, hit that on 5 shards almost immediately, then kept going until 512 shards were serving 118 million queries per second across 1.22 PiB of data. The workload is deliberately narrow: a single-shard point select fetching one row by primary key with no writes, joins, or cross-shard queries, which is what makes the scaling curve clean.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
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&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2FebUMYv/1/010001a09f6732c2-b6d784ad-1e98-4f01-b696-8f450cb5c60d-000000/nzCcgWL-UsC0yXuxnh2CzOIEWneiZRqYd6XpABdOw5g=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Now everyone can put data to work (3 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    ChatGPT Work's Data agent connects to approved company data and business definitions, letting employees investigate questions and build shareable dashboards using plain language. It respects existing permissions, works with major data and BI platforms, and can recommend or carry out approved follow-up actions.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fmotherduck.com%2Fblog%2Fwhy-duckdb-20-is-faster%2F%3Futm_source=tldrdata/1/010001a09f6732c2-b6d784ad-1e98-4f01-b696-8f450cb5c60d-000000/HCxJ0dzulmpAVr8wrZjekWfqKajw7B4qvcvQ1y837vg=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Why DuckDB 2.0 is faster (15 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    DuckDB 2.0 speeds up S3 queries by 2-3x through asynchronous I/O, dramatically improves deep recursive queries, and makes semi-structured VARIANT data smaller and faster than JSON. It also adds practical SQL features including triggers, nested schemas, and data-modifying CTEs.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;🎁&lt;/span&gt;&lt;/div&gt;&lt;/div&gt;
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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;&lt;strong&gt;&lt;h1&gt;Miscellaneous&lt;/h1&gt;&lt;/strong&gt;&lt;/div&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table bgcolor="" style="table-layout: fixed; width: 100%;" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td style="padding:0;border-collapse:collapse;border-spacing:0;margin:0;" valign="top"&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftowardsdatascience.com%2Fyour-ai-adoption-lift-is-a-selection-effect%2F%3Futm_source=tldrdata/1/010001a09f6732c2-b6d784ad-1e98-4f01-b696-8f450cb5c60d-000000/Z5W64Qn6LtKdRgzT8Usb6HAnv3AocaGAodn85o_OIhk=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Your AI Adoption Lift Is a Selection Effect (17 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Comparing AI adopters with non-adopters usually exaggerates impact because more engaged customers are also more likely to adopt. Instead, estimate causality using variation that customers did not choose, such as an eligibility threshold, while clearly limiting conclusions to the population and decision it measures.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Festuary.dev%2Fblog%2Fpostgres-cdc-backfill%3Futm_source=tldrdata/1/010001a09f6732c2-b6d784ad-1e98-4f01-b696-8f450cb5c60d-000000/FEVF_Nvt8YvWBu8DarL8QTUa3kma77sRZbqnQ2Hj9eM=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;PostgreSQL CDC Backfills at Scale: Running a Multi-Day Backfill While Production Keeps Writing (15 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Large PostgreSQL CDC backfills fail less from historical reads than from WAL retention, delayed slot acknowledgments, long-running transactions, and stale snapshot rows racing live changes. Safer designs keep consuming WAL during chunked reads, checkpoint progress, reconcile overlaps with watermarks and fences, and size chunking around real write rates.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;⚡&lt;/span&gt;&lt;/div&gt;&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;

&lt;h1&gt;&lt;strong&gt;Quick Links&lt;/strong&gt;&lt;/h1&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fblog.dataengineerthings.org%2Finvestigating-spark-waste-across-the-stack-188aa622e12a%3Futm_source=tldrdata/1/010001a09f6732c2-b6d784ad-1e98-4f01-b696-8f450cb5c60d-000000/Buz2NDTzux2xg4QlKkbB64TnbRckNDgBTkwrwZmPW6s=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Investigating Spark Waste Across the Stack (10 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Four production Spark cases cut runtime, compute, S3 operations, and costs by tracing hidden waste beyond top-level CPU and memory metrics.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fcovertaltruism.substack.com%2Fp%2Fthe-half-life-of-a-query%3Futm_source=tldrdata/1/010001a09f6732c2-b6d784ad-1e98-4f01-b696-8f450cb5c60d-000000/6_VLgdFEdaROUpekAk7imoeEXuP4VJphYqgjDcBfJog=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;The Half-Life of a Query (7 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Tools like dbt turn fragile, undocumented analysis into reproducible systems, while AI often misses the constraints of real workplaces.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fmuratbuffalo.blogspot.com%2F2026%2F09%2Fjetpack-consensus-made-generally-fast.html%3Futm_source=tldrdata/1/010001a09f6732c2-b6d784ad-1e98-4f01-b696-8f450cb5c60d-000000/YL1BCVDJ_SxPBZyPCBmOUIoanXpujev0Valvg8XwgD0=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Jetpack: Consensus Made Generally Fast (OSDI '26) (4 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Jetpack adds a portable 1-RTT fast-commit path to existing consensus systems, cutting latency for write-heavy workloads by up to 60% while adding redundant work, delaying execution, and requiring careful recovery to remain safe during leader changes.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;






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