Real-Time Dashboards with Apache Pinot

Overview

Apache Pinot is an open-source, distributed OLAP datastore purpose-built for ultra-low-latency analytics on mutable, high-throughput data streams. When paired with a visualization layer (Superset, Grafana, Streamlit, or a custom React app) you can power dashboards that update in seconds or even milliseconds. This article walks through architecture, data modeling, ingestion, query design, and operational best practices for building real-time dashboards with Pinot.

Why Choose Pinot for Real-Time Dashboards?

• Sub-second query latency on billions of rows.
• Pluggable streaming ingestion from Kafka, Kinesis, Pulsar, or SDKs.
• Columnar storage + advanced indexing (star-tree, inverted, JSON) ideal for slice-and-dice analytics.
• ANSI SQL support, so existing BI tools and SQL editors (including Galaxy) can query Pinot via JDBC.
• Horizontal scalability—add servers to handle more data or queries without downtime.

End-to-End Architecture

1. Data Sources

Events originate from applications, CDC pipelines, IoT devices, or logs. They flow into a message bus such as Kafka.

2. Stream Ingestion

Pinot’s RealtimeTable consumes directly from the stream. Schema and table configs define partitioning, indexing, and retention policies.

3. Optional Batch Layer

Historical backfills or cold data can be loaded into OfflineTable segments via Spark, Flink, or Minion ingestion jobs.

4. Query Layer

Client applications connect to the Broker through JDBC, REST, or gRPC. Brokers fan out queries to Servers, merge results, and return a response typically in <100 ms.

5. Visualization

Dashboards poll Pinot periodically or use websocket push to refresh charts. Tools like Superset, Grafana, or a Node.js backend can expose the data to the frontend. If you prefer a developer-centric SQL experience, Galaxy can sit in front of Pinot for query prototyping and performance tuning before wiring the SQL into your app.

Designing the Pinot Schema

1. Choose a primary time column (e.g., event_time) in epoch millis for retention and partitioning.
2. Dimension columns: Attributes you will filter or group by (e.g., page, country, device_type).
3. Metric columns: Numeric fields you will aggregate (latency_ms, bytes_sent).
4. High-cardinality fields: Consider no-dictionary or raw indexing to save memory.
5. Star-tree indexes: Enable for common GROUP BY + WHERE combos to accelerate roll-ups.

Sample Schema JSON

{
"schemaName": "page_views",
"dimensionFieldSpecs": [
{"name": "page", "dataType": "STRING"},
{"name": "country", "dataType": "STRING"},
{"name": "device_type", "dataType": "STRING"}
],
"metricFieldSpecs": [
{"name": "views", "dataType": "INT"},
{"name": "latency_ms", "dataType": "LONG"}
],
"dateTimeFieldSpecs": [
{
"name": "event_time",
"dataType": "LONG",
"format": "1:MILLISECONDS:EPOCH",
"granularity": "1:MILLISECONDS"
}
]
}

Ingesting Streaming Data

Realtime Table Config

{
"tableName": "page_views",
"tableType": "REALTIME",
"segmentsConfig": {
"timeColumnName": "event_time",
"retentionTimeUnit": "DAYS",
"retentionTimeValue": "7"
},
"streamConfig": {
"streamType": "kafka",
"stream.kafka.topic.name": "page_views",
"stream.kafka.broker.list": "kafka:9092",
"stream.kafka.consumer.type": "lowlevel"
}
}

For exactly-once ingestion, enable stream.kafka.consumer.prop.isolation.level=read_committed and checkpoint offsets to ZooKeeper or Kafka topic.

Query Patterns for Dashboards

Rolling Aggregations

SELECT page,
COUNT(*) AS views,
PERCENTILEEST(latency_ms, 95) AS p95_latency
FROM page_views
WHERE event_time > ago('PT15M')
GROUP BY page
ORDER BY views DESC
LIMIT 20;

Top-N With Dimensions

SELECT country, SUM(views) AS total_views
FROM page_views
WHERE event_time BETWEEN now() - INTERVAL '1' DAY AND now()
GROUP BY country
ORDER BY total_views DESC
LIMIT 10;

Live Anomaly Detection

SELECT COUNT(*)
FROM page_views
WHERE event_time > ago('PT30S')
AND page = '/checkout';

Trigger alerts if count deviates ±3 σ from historical baseline.

Integrating Pinot with Galaxy

Because Pinot exposes a standard SQL interface, you can register the Pinot Broker JDBC URL (jdbc:pinot://broker-host:8099/) inside Galaxy. Developers then enjoy:

• AI-assisted SQL: Galaxy’s copilot can suggest GROUP BYs, aggregations, and index hints specific to Pinot SQL grammar.
• Shared Queries: Save and endorse production dashboard queries in Collections so teammates don’t reinvent the wheel.
• Version History: Track changes to critical KPI queries, roll back mistakes, and compare runtime stats.

Once a query is tuned in Galaxy, copy the SQL into your dashboard codebase or BI tool.

Operational Best Practices

Segment Tuning

• Keep realtime segment flush size around 100 MB or 30 min, whichever comes first.
• Merge small segments nightly using Minion to improve scan efficiency.

Index Strategy

• Use sorted column on time to prune ranges quickly.
• Enable star-tree on frequently aggregated columns.
• Apply text index or FST index for LIKE/REGEXP filters.

Capacity Planning

Rule of thumb: 1 CPU core per 4 K QPS at P95 under 50 ms for simple aggregations. Memory should be 3× segment size for pinning dictionaries and indexes.

Common Pitfalls and How to Avoid Them

Using Pinot as OLTP Storage

Pinot is optimized for append-only analytics, not high-update transactional workloads. If you need random row updates, keep them in MySQL/Postgres and replicate CDC into Pinot.

Ignoring Index Warm-up

After cluster restarts, cold caches can spike latency. Perform a warm-up script that sends representative queries to pre-populate indexes.

Over-Aggregating on the Client

Some teams fetch raw events into a dashboard and aggregate in JavaScript—wasting bandwidth and compute. Instead, push aggregations into Pinot where it is vectorized and indexed.

Putting It All Together

With stream ingestion configured, schema tuned, and queries optimized in Galaxy, you can wire your visualization layer to call Pinot every few seconds. The result is a dynamic, always-fresh dashboard showing KPIs, user behavior, or operational metrics with latency measured in milliseconds.