Trino-based Modern Data Platform architecture design — decoupled storage/compute, Iceberg as open table format, Hive Metastore/Glue catalog, MinIO/S3 object storage, Kafka ingestion layer, dbt transformation, Airflow orchestration, Superset BI — coordinator/worker topology, catalog design, metadata-driven architecture, multi-layer lakehouse (Bronze/Silver/Gold), federated query across heterogeneous sources
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Trino observability and monitoring platform — JMX Prometheus exporter configuration (running queries/failed queries/OOM kills/execution latency P50/P90/P99/memory pool metrics), Grafana dashboard panels, OpenTelemetry trace propagation, query-level event listener for structured logging, Prometheus alert rules (worker loss/queue depth/OOM/failure rate/p99 latency), log aggregation patterns, query history analysis via REST API, slow query detection SQL
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Trino production readiness checklist and review — coordinator HA (active/passive with load balancer), worker autoscaling, TLS/HTTPS configuration, authentication setup, resource groups for multi-tenancy, JVM sizing, spill configuration, fault-tolerant execution for batch workloads, monitoring (Prometheus/alerting), graceful shutdown, backup strategy for Hive Metastore, catalog security, query history retention, log rotation, Kubernetes deployment checklist
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Trino memory management and spill-to-disk tuning — query.max-memory/query.max-total-memory/query.max-memory-per-node properties, memory.heap-headroom-per-node, JVM heap sizing (80% of RAM), spill-enabled/spiller-spill-path/spill-compression-codec configuration, exchange buffer tuning, OOM kill diagnosis, memory pool sizing, fault-tolerant execution exchange manager (S3/filesystem), session-level memory overrides, memory-intensive operator patterns (HashJoin/Sort/Window/GroupBy)
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Trino resource group workload governance — resource-groups.properties configuration (maxQueued/hardConcurrencyLimit/softMemoryLimit/hardCpuLimit/schedulingPolicy/schedulingWeight), hierarchical group trees, selector rules (user/source/queryType/clientTags/regex), multi-tenant tenant isolation patterns, per-user dynamic sub-groups (${USER} template), scheduling policies (fair/weighted_fair/weighted/query_priority), CPU quota periods, database-backed configuration (MySQL/PostgreSQL), JMX monitoring of group utilization
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Trino security hardening and data governance — TLS/HTTPS setup, internal-communication shared secret, authentication (LDAP/OAuth2/JWT), file-based access control (catalog/schema/table/column rules, column masking, row-level filtering, query kill permissions), OPA integration, Ranger for dynamic row-filter and column-mask, catalog isolation by domain, user group mapping, impersonation rules, audit logging via event listener, password file authentication for development
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Autonomous self-healing Trino platform — Python watchdog agents for automatic worker restart detection, hung query killer, OOM-killed query resubmission, Iceberg compaction auto-trigger on small-file detection, stale statistics auto-ANALYZE, cluster memory pressure relief (kill low-priority queries), anomaly detection on query latency (Z-score), Claude-based RCA generation for incidents, Prometheus AlertManager webhook integration, Airflow self-healing sensor
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Trino distributed SQL query optimization — predicate/projection/aggregation pushdown, join reordering (AUTOMATIC/ELIMINATE_CROSS_JOINS), broadcast vs partitioned joins, dynamic filtering, CBO with ANALYZE, filter-early patterns, partition pruning, avoiding SELECT *, reducing shuffle, cross-catalog query cost, session property tuning, query hints, anti-patterns for slow Trino queries
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Trino Modern Data Stack end-to-end reference architecture — full docker-compose (Kafka + MinIO + Hive Metastore + Trino + Airflow + dbt + Superset + Prometheus + Grafana), medallion lake DDL and pipeline DAGs, Kafka→Iceberg streaming ingest, dbt project layout with Trino profiles, Superset dataset config, Prometheus alert rules, production Kubernetes Helm values, operational runbook for daily maintenance
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StarRocks memory tuning — BE memory architecture (mem_limit, query pool, load pool, compaction pool, metadata cache), query memory limits (query_mem_limit session var), memory spill to disk (spill_mode), OOM analysis (be.out log), mem_tracker hierarchy, FE heap tuning (JAVA_OPTS), Primary Key table persistent index memory, jemalloc tuning
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StarRocks data modeling — star schema vs wide table design, fact/dimension DDL patterns, denormalization strategy for OLAP, bitmap indexes, bloom filter indexes, sort key optimization, schema review checklist, SCD type 2 with Primary Key table, aggregation table design, BI acceleration patterns, avoiding over-normalization
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StarRocks bucketing (distribution) — DISTRIBUTED BY HASH vs RANDOM, bucket count selection formula, distribution key selection (high cardinality/join key), skew detection and prevention, colocate join setup (colocate_with property), bucket count modification (ALTER TABLE), auto bucket sizing (StarRocks 3.2+), tablet sizing target (1-10 GB)
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StarRocks table types — Duplicate Key (append-only log/event), Aggregate Key (pre-aggregation metrics), Unique Key (upsert dedup), Primary Key (full DML upsert/delete, MVCC), CREATE TABLE DDL, key column selection, aggregate functions (SUM/MAX/MIN/REPLACE/HLL_UNION/BITMAP_UNION), when to use each type, storage engine differences
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StarRocks EXPLAIN plan reading — EXPLAIN/EXPLAIN VERBOSE/EXPLAIN COSTS output structure, operator nodes (OlapScanNode/HashJoinNode/AggregationNode/ExchangeNode/SortNode/ProjectNode), reading fragment instances, identifying bottlenecks (shuffle exchange cost, large scan rows, memory spill), runtime filter nodes, partition pruning in scan, query profile (SHOW PROFILELIST/EXPLAIN ANALYZE)
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StarRocks data quality — freshness queries (MAX(updated_at) vs expected SLA), duplicate detection on Primary Key tables, null anomaly SQL (column-level null rate vs baseline), volume drift detection (day-over-day ratio), cross-table referential integrity checks, data completeness for partitions, StarRocks-specific quality checks (tablet health, compaction lag, replication factor), Python DQ scan class
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StarRocks real-time analytics — Kafka → Routine Load → Primary Key table for sub-second freshness, low-latency BI query patterns, real-time dashboard design (materialized view for pre-aggregation), metric store patterns, window-based freshness for streaming dashboards, colocate join for real-time multi-table queries, resource group isolation for OLAP vs ingestion
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StarRocks aggregation optimization — one-phase vs two-phase aggregation, pre-aggregation pushdown to scan, Aggregate Key table automatic merge, GROUP BY on distribution key (local aggregation), COUNT DISTINCT optimization (multi-phase), HLL/BITMAP approximate aggregation, aggregation spill tuning, EXPLAIN aggregation nodes
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StarRocks security and RBAC — CREATE USER/ROLE, GRANT/REVOKE privileges (catalog/database/table/view/MV/function/resource group level), built-in roles (cluster_admin/db_admin/user_admin/public), LDAP/LDAP group auth, row-level security policies, column masking, SSL/TLS, audit log, privilege inheritance, security best practices
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StarRocks storage balancing — tablet distribution across BEs, disk skew detection (SHOW BACKENDS disk usage), cluster_balance analysis (SHOW PROC '/cluster_balance'), balance_load_score_threshold tuning, tablet migration monitoring, BE decommission, adding new BE nodes, storage capacity forecasting, imbalance alerts
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StarRocks AI incident RCA — autonomous root cause analysis agent for FE/BE failures, memory spike diagnosis (BE heap/process memory), Routine Load pause RCA (ErrorLogUrls + error patterns), Kafka consumer lag spike analysis, compaction backlog detection, tablet replication failures, OOM kill patterns, query timeout clusters, FE GC pressure detection, structured incident report generation
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dbt + StarRocks production readiness — slim CI with state:modified+, manifest.json artifact storage and --defer for dev, dbt Cloud vs dbt Core deployment, RBAC for dbt users in StarRocks, secrets management for profiles.yml, dbt run/test exit codes in CI, breaking change detection, scheduled dbt runs via Airflow/dbt Cloud, dbt docs generation and hosting, model versioning
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Airflow + StarRocks pipeline orchestration — MySqlHook for DDL/DML, Broker Load trigger + poll pattern, Stream Load via HTTP operator, Routine Load job lifecycle (pause/resume/stop), sensor for load completion, ANALYZE TABLE post-load, partition-aware DAG templating, Airflow variables for StarRocks connection config, XCom label passing
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StarRocks backup and restore — CREATE REPOSITORY (S3/HDFS/NFS), BACKUP DATABASE/TABLE, SHOW BACKUP/RESTORE status, RESTORE with partition granularity, cross-cluster restore, incremental backup strategy, snapshot lifecycle, disaster recovery RTO/RPO targets
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StarRocks cluster health monitoring — FE/BE/CN node status, tablet health, replica status, compaction backlog, storage imbalance, SHOW commands, system tables (information_schema/sys), alert thresholds, FE quorum checks, BE disk usage, dead replica detection, Prometheus metrics
日本語の概要は準備中です。原文の説明を表示しています。