clickhouseaitelemetry

Quick-start: pipeline telemetry from desktop AI assistants into ClickHouse for experimentation

UUnknown

2026-02-20

9 min read

Compact how-to to send assistant interactions, exposures and errors into ClickHouse for fast experiments and quick regression diagnosis.

Hook: stop guessing why your desktop assistant regressed — collect the right telemetry

If your product and ML teams are scrambling to diagnose regressions after a desktop assistant update, you are missing signal. Without compact, reliable telemetry that captures user interactions, exposure decisions, and errors, experiments stall and rollbacks are risky. This how-to shows you, in practical steps, how to collect assistant telemetry into ClickHouse in 2026 so teams can run experiments, measure impact and diagnose regressions within minutes — not days.

Why ClickHouse for assistant telemetry in 2026

ClickHouse is the OLAP workhorse for high-cardinality, high-throughput event workloads. In late 2025 the project and its cloud vendors secured new funding and product investment, reinforcing ClickHouse as a go-to for analytics at scale. For assistant telemetry, ClickHouse offers:

Fast inserts and queries for millions of short-lived events per second.
Flexible ingestion via HTTP, Kafka engine, and native connectors.
Compression and TTLs to control storage cost for raw events while keeping aggregates.
Materialized views and pre-aggregations so experiments run in seconds.

High-level pipeline: from desktop assistant to experiment-ready tables

Design the pipeline in stages. Each stage has clear responsibilities so product and ML teams can trust the data.

Event capture inside the desktop assistant (Electron, macOS, Windows native), emitting interaction, exposure, and error events.
Edge aggregation on-device batching, PII redaction and rate limiting to reduce traffic and protect privacy.
Transport to a streaming layer (Kafka / Confluent / AWS Kinesis) or directly to ClickHouse HTTP insert endpoint.
Ingestion into ClickHouse using the Kafka table engine or HTTP insert with JSONEachRow or Protobuf for efficiency.
Materialized views and aggregate tables for experiment metrics and dashboards (Grafana, Superset).

Event taxonomy and recommended schema

Be explicit about the signals you need for experimentation and debugging. At minimum, collect three event classes:

exposure: when the assistant decides to show a feature, variant, or model (A/B assignment)
interaction: user message, assistant response metadata, latency, tokens used
error: exceptions, failed API calls, model timeouts, stack traces

Minimal JSON event example

{
  "event_id": "uuid-v4",
  "event_type": "interaction",
  "timestamp": "2026-01-18T12:34:56.789Z",
  "assistant_id": "desktop-1",
  "assistant_version": "1.3.0",
  "user_id_hash": "sha256-abcd...",
  "session_id": "sess-123",
  "experiment_id": "exp-voiceprompt-2026",
  "variant": "B",
  "prompt_tokens": 45,
  "response_tokens": 120,
  "latency_ms": 920,
  "model": "gptx-1000",
  "outcome": "success"
}

Notes:

Use a stable event_id UUID so you can dedupe later.
Never store raw PII. Hash or remove user identifiers before sending.
Include assistant_version and model to attribute regressions.

ClickHouse table design: raw stream, deduped store, aggregates

Store raw events in a MergeTree table, but build a streaming ingestion path that handles duplicates and schema evolution.

1) Raw events table (MergeTree)

CREATE TABLE assistant_events_raw (
  event_id String,
  event_type String,
  event_time DateTime64(3),
  assistant_id String,
  assistant_version String,
  user_id_hash String,
  session_id String,
  experiment_id Nullable(String),
  variant Nullable(String),
  payload String
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_time)
ORDER BY (assistant_id, event_time, event_id)
TTL event_time + INTERVAL 90 DAY;

This table keeps raw JSON payloads in the payload column so you can reparse fields for ad hoc debugging. A 90-day TTL is a starting point; keep longer aggregates only.

2) Kafka ingestion flow

Use ClickHouse Kafka engine to stream events from your broker and a Materialized View to insert into the MergeTree. This gives backpressure resilience and near-real-time ingestion.

CREATE TABLE kafka_events (
  event_id String,
  event_type String,
  event_time DateTime64(3),
  assistant_id String,
  assistant_version String,
  user_id_hash String,
  session_id String,
  experiment_id Nullable(String),
  variant Nullable(String),
  payload String
) ENGINE = Kafka SETTINGS
  kafka_broker_list = 'kafka:9092',
  kafka_topic_list = 'assistant-events',
  kafka_group_name = 'ch-consumer',
  format = 'JSONEachRow';

CREATE MATERIALIZED VIEW mv_kafka_to_raw TO assistant_events_raw AS
SELECT * FROM kafka_events;

3) Deduped events via ReplacingMergeTree

When idempotence is required, write into a ReplacingMergeTree keyed by event_id so later duplicates are collapsed. Useful for client retries.

CREATE TABLE assistant_events_dedup (
  event_id String,
  event_type String,
  event_time DateTime64(3),
  assistant_id String,
  assistant_version String,
  user_id_hash String,
  session_id String,
  experiment_id Nullable(String),
  variant Nullable(String),
  payload String
) ENGINE = ReplacingMergeTree(event_time)
PARTITION BY toYYYYMM(event_time)
ORDER BY (event_id);

Client-side code: compact Electron example

Desktop assistants have unique constraints: network variability, privacy, and CPU. Batch events on-device, strip PII, and send compressed JSON. Here is a compact Node.js example showing batching and exponential backoff to an HTTP ClickHouse endpoint using JSONEachRow.

import fetch from 'node-fetch';
import zlib from 'zlib';

const BATCH_SIZE = 50;
const FLUSH_MS = 5000;
let buffer = [];

function sha256(s) { /* implement or use crypto */ }

async function sendBatch(events) {
  const body = events.map(e => JSON.stringify(e)).join('\n');
  const gz = zlib.gzipSync(body);

  const url = 'https://clickhouse.example.com?query=INSERT%20INTO%20assistant_events_raw%20FORMAT%20JSONEachRow';
  let attempts = 0;
  while (attempts < 5) {
    try {
      const res = await fetch(url, {
        method: 'POST',
        headers: { 'Content-Encoding': 'gzip', 'Content-Type': 'application/json' },
        body: gz
      });
      if (res.ok) return;
      attempts++;
      await new Promise(r => setTimeout(r, Math.pow(2, attempts) * 100));
    } catch (err) {
      attempts++;
      await new Promise(r => setTimeout(r, Math.pow(2, attempts) * 100));
    }
  }
  // last resort: write to local disk for later upload
}

setInterval(() => {
  if (buffer.length === 0) return;
  const toSend = buffer.splice(0, BATCH_SIZE);
  sendBatch(toSend);
}, FLUSH_MS);

export function captureEvent(e) {
  // sanitize e: remove PII, hash user id
  e.user_id_hash = sha256(e.user_id || '');
  delete e.user_id;
  e.event_id = e.event_id || generateUuid();
  buffer.push(e);
}

Experiment metrics: SQL patterns you need

With data in ClickHouse you can compute experiment metrics quickly. Example: exposure to success conversion by variant.

SELECT
  variant,
  countIf(event_type='exposure') as exposures,
  countIf(event_type='interaction' AND outcome='success') as successes,
  round(100.0 * successes / exposures, 3) as conversion_pct
FROM assistant_events_raw
WHERE experiment_id = 'exp-voiceprompt-2026'
  AND event_time >= now() - INTERVAL 7 DAY
GROUP BY variant
ORDER BY variant;

For rolling metrics and alerting, create materialized views that maintain daily counts. Use these to feed dashboards and automated A/B tests that flag regressions in model response latency or error rate.

Handling common operational problems

Schema evolution: publish a schema registry or use Protobuf/Avro on Kafka to enforce typed fields.
Clock skew and late events: use event_time provided by client but also record server_ingest_time. For windowed calculations, use greatest(event_time, server_ingest_time - allowed_skew).
Duplicates: include event_id and use ReplacingMergeTree or dedupe queries using argMax or group by event_id.
Backpressure: prefer Kafka or Confluent to buffer bursts. ClickHouse Kafka engine provides consumer semantics and partitioning.

Privacy, governance and compliance

Assistant telemetry often touches PII and sensitive content. Implement these rules:

Hash user identifiers client-side with a salted SHA256 kept out of ClickHouse.
Strip raw user content unless explicitly needed for labeling; if required, encrypt payloads and restrict access via RBAC.
Document retention policies with TTLs and keep audit logs. ClickHouse supports TTL on columns and rows; use them.
Maintain an audit trail of who ran schema changes and materialized view updates.

Scaling and cost control

ClickHouse reduces cost through high compression, but raw event cardinality can explode. Practical controls:

Keep raw events for a short period (30-90 days) and persist aggregates longer.
Pre-aggregate common experiment metrics via Materialized Views.
Partition by month and use compact order keys to accelerate deletes and merges.
Use sampling for low-signal debug traces and store full traces only for flagged failures.

Diagnosing regressions fast: example playbook

When a regression is reported after a desktop assistant release, run this checklist:

Query error rates by assistant_version and model in the last 24 hours.
Check experiment exposures to see if variant assignment shifted for a cohort.
Inspect latency distributions and top error messages using GROUP BY and TOPK.
Fetch raw payloads for representative event_ids only after approval and with decryption keys.
Roll forward a canary version to 1% and compare key metrics using pre-aggregated tables.

Rule of thumb: with correct telemetry, you should be able to detect a model-level regression within the first hour and confidently triage whether it is caused by model change, infra, or client code.

Advanced strategies and 2026 trends

Several developments in late 2025 and early 2026 change how you design telemetry:

Rise of desktop AI apps: Anthropic and other vendors launched desktop assistants with local file access, increasing the need for careful telemetry that balances helpful signal and privacy.
Assistant models bundled by platform vendors: with Siri integrating third-party models such as Gemini, you must attribute telemetry by model and vendor.
ClickHouse investment and cloud maturity: more managed ClickHouse options simplify operations and make real-time experimentation more accessible.
On-device aggregation and privacy preserving analytics: compute sketches on-device and ship summaries to reduce telemetry volume and preserve privacy.

Mini case study: Atlas Assist cuts MTTI from 6h to 20m

Atlas Assist, a hypothetical desktop assistant provider, instrumented its Electron client with the pipeline above. After shipping:

They reduced mean-time-to-insight (MTTI) from 6 hours to 20 minutes by moving to Kafka + ClickHouse materialized views.
Error triage improved: automatic grouping by top 5 errors covered 82% of incidents.
Experiment velocity increased: product teams launched safe canaries with automatic gating based on pre-defined thresholds.

Actionable rollout checklist

Define event taxonomy and required fields for exposures, interactions, errors.
Implement client-side batching, hashing, and throttling.
Choose transport: Kafka if you need buffering and decoupling; HTTP for low-latency prototypes.
Create ClickHouse raw table and Kafka engine, then materialized views for aggregates.
Set TTLs and implement RBAC for sensitive fields.
Build dashboards and alerting for key metrics (error rate, latency, conversion by variant).
Run a 1% canary and monitor pre-defined metrics before ramping up.

Useful SQL snippets

Quick queries you will run often:

-- Error rate by assistant_version
SELECT assistant_version,
  countIf(event_type='error') as errors,
  count() as total,
  round(100.0 * errors / total, 3) as error_pct
FROM assistant_events_raw
WHERE event_time >= now() - INTERVAL 1 DAY
GROUP BY assistant_version
ORDER BY error_pct DESC;

-- Latency distribution for a model
SELECT quantiles(0.5,0.75,0.9,0.99)(latency_ms) as q
FROM assistant_events_raw
WHERE model = 'gptx-1000' AND event_time >= now() - INTERVAL 1 HOUR;

Final thoughts and predictions for 2026

Telemetry is the nervous system for desktop assistants. As assistants become more integrated into workflows and platforms, product teams must invest in compact, privacy-first event pipelines. ClickHouse is now a mainstream choice for experiment-grade analytics thanks to its performance and ecosystem momentum in 2025. Expect more managed ClickHouse offerings, better connectors to LLM observability tools, and standard telemetry schemas for assistant signals in 2026.

Next steps (call to action)

Start a POC this week: spin up a ClickHouse Cloud instance, create the raw table DDL above, and instrument your desktop assistant with the minimal JSON event. If you want a pre-built starter repo with ClickHouse DDLs, Kafka config and an Electron example, request the quick-start kit from our team at toggle.top/developers or clone the sample repository linked in the footer. Ship safer, diagnose faster, and run experiments with confidence.

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