PG Armenia x Percona University – Yerevan, August 28, 2025
as the organisers put it
Join Percona and PG Armenia for a one-day in-person event filled with sharp talks and hands-on sessions on PostgreSQL, MongoDB, MySQL, and observability.
who it's for
database engineers and DBAs, ClickHouse users and architects, MySQL DBAs and performance tuners, MongoDB administrators, observability engineers, platform engineers and Kubernetes operators, data engineers building real-time pipelines, site reliability engineers
topics
metrics logs traces profiling; Prometheus-based metrics collection; OpenTelemetry tracing instrumentation; eBPF for agentless observability; ClickHouse heatmaps and flamegraphs; Percona Server for MongoDB key management (KMIP Vault); workload-based MySQL tuning; LLM-assisted configuration tuning; Project Antalya querying Iceberg on S3; infinity table MergeTree and Iceberg integration
| title | author | length |
|---|---|---|
| What Developers Should Know About Observability | Peter Zaitsev | 33m |
Covers practical claims: four pillars (metrics, logs, traces, profiling) and why profiling gives line‑level hotspots while metrics give high‑level health… | ||
| Welcome & State of Open Source Databases | Peter Zaitsev | 56m |
Fast, concrete survey of where open source databases move: rising designs for distributed by default systems and separation of storage and compute (Neon sold… | ||
| Finding Bottlenecks in ClickHouse: Profiling, Heatmaps & Flamegraphs | Eugene Klimov | 37m |
Practical checklist for finding ClickHouse bottlenecks: enable telemetry and trace logs and use internal system tables plus the embedded Prometheus exporter to… | ||
| Key Management for Data-at-Rest Encryption in Percona Server for MongoDB | Konstantin Trushin | 33m |
Percona Server for MongoDB encrypts each database with a per-database AES key that is stored in an encryption key DB and protected by a master key kept in KMIP… | ||
| Workload-Based MySQL Tuning | Dmitrii Kochetov | 17m |
A workload-aware tuning pipeline maps a target workload to a closest past run using DBMS internal metrics, prunes redundant signals with factor analysis +… | ||
| Building Real-Time Data Lake with ClickHouse | Alexander Zaitsev | 17m |
Claims compute–storage separation for ClickHouse while keeping real‑time speed by using Project Antalya to query Iceberg on S3 with Parquet files, adding… | ||
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