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Manta V1 Limitations and Missing Features

Updated: 2026-01-12

Architecture Limitations

The system operates as a "Single-Node Only" setup without replication, sharding, or distributed queries. Additionally, there is "No Query Admission Control" - meaning no concurrent query limits, memory budgets per query, or queueing mechanisms.

SQL Coverage Gaps

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Manta Query CPU Hotspots

Profiling run: 2026-01-03 Branch: feat/benchmark-messagepack-ingest Git SHA: 95efdca Data: 1M rows Events dataset (standard preset)

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1. LOWER(UPPER(region)) GROUP BY (~106ms)

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{
  "benchmark_info": {
    "tool": "ultimate_perf_test --quick",
    "iterations_per_query": 20,
    "metric": "TrimMean (ms)",
    "dataset": {
      "total_rows": 100000,
      "total_columns": 106,
      "core_columns": 12,
      "sparse_metrics": 90

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{
  "benchmark_info": {
    "tool": "ultimate_perf_test --quick",
    "iterations_per_query": 20,
    "metric": "TrimMean (ms)",
    "dataset": {
      "total_rows": 100000,
      "total_columns": 106,
      "core_columns": 12,
      "sparse_metrics": 90

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{
  "benchmark_date": "2025-12-29T22:07:18.976341494+00:00",
  "branches": {
    "main": "c809a5f",
    "optimizations": "1ecc713"
  },
  "dataset": {
    "rows": 100000,
    "columns": 106,
    "min_cols_per_row": 16,

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Manta Optimization Plan: Cutting-Edge Analytics Database

Executive Summary

This plan outlines research-backed optimizations to make Manta a cutting-edge analytics database. Based on extensive research of academic papers, industry blog posts, and multi-model AI consensus (Gemini-3-Pro + GPT-5.2), we recommend a prioritized roadmap focusing on "skip work" optimizations that outperform micro-optimizations.

Current State: Manta achieves 7-8M rows/sec with sparse columnar storage, time-partitioned slabs, HyperLogLog, T-Digest, and SIMD_LANES=4.

Target State: 2-10x performance improvement through zone maps, lazy materialization, and adaptive compression.

manish

Manta: In-Memory Analytics Database

Fast, graceful, glides through data — inspired by Facebook Scuba

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EXECUTIVE SUMMARY

What We're Building

A Scuba-inspired, single-node, in-memory analytics database in Rust. Optimized for high-throughput event ingestion and fast aggregation queries.

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AskMarkAI - Slack App Registration Instructions

This guide walks through registering a Slack app for the AskMarkAI integration.

1. Create the Slack App

• Go to https://api.slack.com/apps
• Click "Create New App"
• Choose "From scratch"
• App Name: AskMarkAI (or your preferred name)
• Pick a workspace to develop your app in

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🔍 Complete Upload Workflow Trace Analysis - AskMarkAI

Analysis Date: $(date)
System: AskMarkAI Document Upload & Processing Pipeline
Scope: Text Content, File Attachments, and URL Processing workflows

Executive Summary

This comprehensive trace analysis reveals how AskMarkAI handles three distinct document upload types through a unified, event-driven architecture. All upload paths converge at a single document processor triggered by S3 events, ensuring consistent processing and vector embedding generation.

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Deletion Workflow Analysis for AskMarkAI Document System

Overview

This analysis examines the deletion workflow for three content types in the AskMarkAI document system:

1. Text Content - Direct text input
2. File Attachments - PDF/DOC files uploaded via presigned URLs
3. URL Processing - HTML content extracted from web URLs

Complete Deletion Flow