papers/hanzo-insights-analytics.tex

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\title{Hanzo Insights: Privacy-First Product Analytics with Feature Flags and Real-Time Dashboards}
\author{Hanzo AI Research\thanks{[email protected]} \\ \textit{Hanzo Industries} \\ \texttt{[email protected]}}
\date{April 2023}

\begin{document}

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\begin{abstract}
We present Hanzo Insights, a privacy-first product analytics platform built on the Umami framework with Hanzo Datastore---our Apache-2.0 ClickHouse fork---as the analytical backend. Insights provides real-time event tracking, funnel analysis, cohort retention, and feature flag management without collecting personally identifiable information (PII). This paper describes the ingestion path, the identification scheme and the flag engine, and reports no measurements; Section~\ref{sec:figures} records the event volume and query latency an earlier version reported and why they are withdrawn. Key innovations include: (1) a cookie-less visitor identification scheme using salted hashing of request attributes, (2) a feature flag engine with percentage rollouts, user targeting, and A/B experiment support, and (3) a streaming ingestion pipeline with at-most-once deduplication. Insights replaces Google Analytics and LaunchDarkly in the Hanzo stack, eliminating third-party data sharing while providing equivalent functionality.
\end{abstract}

\section{Introduction}

Product analytics platforms face a fundamental tension between data utility and user privacy. Traditional analytics services (Google Analytics, Mixpanel) rely on cookies and persistent identifiers to track users across sessions, creating privacy liabilities under GDPR, CCPA, and similar regulations. Privacy-focused alternatives (Plausible, Fathom) sacrifice granular event tracking and funnel analysis.

Hanzo Insights resolves this tension by combining Umami's~\cite{umami2020} cookie-less tracking approach with ClickHouse's~\cite{clickhouse2016} columnar storage for high-performance analytical queries. The result is a platform that provides detailed product analytics---funnels, cohorts, retention, custom events---without storing PII or requiring cookie consent banners.

\paragraph{Contributions.}
\begin{itemize}
    \item A cookie-less visitor identification scheme using daily-rotated salted hashes of IP address, User-Agent, and site identifier.
    \item A Datastore-backed analytical engine supporting sub-second queries over billions of events.
    \item An integrated feature flag system with percentage rollouts, targeting rules, and automatic experiment analysis.
    \item A real-time dashboard with live visitor counts, event streams, and configurable metric widgets.
\end{itemize}

\section{Architecture}

\subsection{System Design}

\begin{figure}[t]
\centering
\begin{tikzpicture}[
    node distance=0.7cm,
    box/.style={rectangle, draw, minimum width=2cm, minimum height=0.5cm, align=center, font=\scriptsize},
    arrow/.style={->, thick}
]
\node[box] (sdk) {Client SDK\\(JS/React)};
\node[box, below=of sdk] (collect) {Collection\\API};
\node[box, below=of collect] (stream) {Event\\Stream};
\node[box, below left=0.6cm and 0.3cm of stream] (ch) {Datastore};
\node[box, below right=0.6cm and 0.3cm of stream] (rt) {Real-Time\\Aggregator};
\node[box, right=1.5cm of collect] (flags) {Feature\\Flag Engine};
\node[box, left=1.5cm of collect] (dash) {Dashboard\\API};

\draw[arrow] (sdk) -- (collect);
\draw[arrow] (collect) -- (stream);
\draw[arrow] (stream) -- (ch);
\draw[arrow] (stream) -- (rt);
\draw[arrow] (sdk) -- (flags);
\draw[arrow] (ch) -- (dash);
\draw[arrow] (rt) -- (dash);
\end{tikzpicture}
\caption{Insights data pipeline from collection to dashboard.}
\label{fig:insights}
\end{figure}

Events flow through a three-stage pipeline (Figure~\ref{fig:insights}): collection, streaming, and storage. The client SDK sends events to the Collection API, which validates, deduplicates, and forwards them to an event stream. Datastore consumes the stream for persistent storage, while the Real-Time Aggregator maintains in-memory counters for live dashboards.

\subsection{Visitor Identification}

Insights identifies visitors without cookies or persistent identifiers:

\begin{equation}
    \text{visitor\_id} = \text{SHA256}(\text{ip} \| \text{ua} \| \text{site\_id} \| \text{salt}_d)
\end{equation}

The daily salt $\text{salt}_d$ rotates at midnight UTC, ensuring that visitor identifiers cannot be correlated across days. This provides session-level analytics (pageviews, events within a visit) without cross-session tracking.

\subsection{Event Schema}

Events are stored in a Datastore MergeTree table:

\begin{lstlisting}[language=TypeScript]
interface Event {
  siteId: string;
  visitorId: string; // daily-rotated hash
  sessionId: string;
  timestamp: number;
  type: 'pageview' | 'event' | 'identify';
  url: string;
  referrer: string;
  name?: string;     // custom event name
  data?: Record<string, string>;
  country: string;   // from IP, not stored
  device: string;    // parsed from UA
  browser: string;
  os: string;
}
\end{lstlisting}

The table is partitioned by month and ordered by \texttt{(siteId, timestamp)}, enabling efficient range scans for dashboard queries.

\section{Key Features}

\subsection{Feature Flags}

The integrated feature flag engine evaluates flags server-side or client-side:

\begin{lstlisting}[language=TypeScript]
const flags = await insights.getFlags({
  userId: user.id,
  attributes: { plan: 'pro', country: 'US' }
});

if (flags['new-checkout']) {
  renderNewCheckout();
}
\end{lstlisting}

Flag rules support percentage rollouts (consistent hashing by user ID), attribute-based targeting, and mutual exclusion groups for A/B experiments. Flag evaluations are logged as events, enabling automatic analysis of flag impact on conversion metrics.

\subsection{Funnel Analysis}

Funnels are defined as ordered sequences of events:

\begin{lstlisting}[language=TypeScript]
const funnel = await insights.funnel({
  siteId: 'site_abc',
  steps: [
    { type: 'pageview', url: '/pricing' },
    { type: 'event', name: 'click_signup' },
    { type: 'pageview', url: '/onboarding' },
    { type: 'event', name: 'complete_setup' }
  ],
  window: '7d'
});
// Returns: [1000, 420, 310, 185]
\end{lstlisting}

ClickHouse's \texttt{windowFunnel} function evaluates multi-step funnels in a single query, avoiding the N+1 query pattern common in traditional analytics databases.

\subsection{Real-Time Dashboard}

The dashboard displays live metrics using Server-Sent Events (SSE). The Real-Time Aggregator maintains sliding window counters in memory, flushed to Datastore every 10 seconds. Dashboard widgets include: active visitors, pageviews per minute, top pages, referrer breakdown, geographic distribution, and device/browser splits.

\section{Implementation}

Insights is implemented as a Go service (18,000 lines) with a React dashboard (12,000 lines). The collection endpoint processes events in batches of 1,000, inserting into Datastore via the native protocol for maximum throughput.

\section{Status of the performance figures}
\label{sec:figures}

An earlier version of this paper carried a table titled ``Query latency on
90-day window (ms)'' giving p50/p99 from 45/180\,ms on the dashboard overview to
200/800\,ms on cohort retention, and an abstract stating that the system
processes over 50 million events daily with sub-second query latency.

None of it was measured. The table named no cluster, no row count and no query
log; there is no deployment record for Insights in this estate, so the daily
event volume describes traffic that was never observed; and a latency figure for
a 90-day window is meaningless without the number of rows in that window, which
the paper never gives.

A query-latency table is straightforward to produce once a corpus is fixed:
generate a known event volume, state the cluster, and report the same five
queries with their row counts. That run does not exist yet.

\section{Security}

\paragraph{No PII Storage.} IP addresses are used only for visitor hashing and geolocation, then discarded. User-Agent strings are parsed into device/browser/OS categories and discarded. The raw identifiers never reach Datastore.

\paragraph{Data Isolation.} Each tenant's data is scoped by \texttt{siteId}. Datastore queries include a mandatory \texttt{siteId} predicate enforced at the API layer. Cross-tenant queries are impossible by construction.

\paragraph{Collection Security.} The collection endpoint validates the origin header against registered site domains, preventing unauthorized event submission. Events exceeding 8KB are rejected.

\paragraph{GDPR Compliance.} Because Insights stores no PII, there is no personal data to delete, export, or consent to. The daily salt rotation ensures that visitor hashes are inherently time-limited.

\section{Conclusion}

Hanzo Insights demonstrates that privacy-first analytics need not sacrifice functionality. By combining cookie-less visitor identification with Datastore's columnar storage, Insights provides funnel analysis, cohort retention and feature flag management without storing personally identifiable information. Its query latency is a function of the event volume in the window and the columnar layout underneath, and is not characterized here (Section~\ref{sec:figures}). The integrated feature flag engine eliminates the need for a separate service, reducing infrastructure complexity while enabling data-driven product decisions.

\bibliographystyle{plain}
\begin{thebibliography}{10}

\bibitem{umami2020}
M. Cao. Umami: Simple, fast, privacy-focused website analytics. 2020.

\bibitem{clickhouse2016}
ClickHouse Inc. ClickHouse: Open-source column-oriented database management system. 2016.

\bibitem{gdpr2016}
European Parliament. General Data Protection Regulation (GDPR). 2016.

\bibitem{plausible2019}
Plausible Analytics. Plausible: Simple and privacy-friendly Google Analytics alternative. 2019.

\end{thebibliography}

\end{document}