
Skill
doris-best-practices
optimize Apache Doris table design and clusters
Description
Apache Doris table design and cluster sizing best practices. MUST USE when writing, reviewing, or optimizing Doris CREATE TABLE statements, partition/bucket strategies, data models, or cluster configurations. ALSO MUST USE whenever the doris-architecture-advisor skill produces DDL — apply the Pre-Flight Checklist to every CREATE TABLE before output. Also triggers on any workload design involving: IoT, analytics, dashboard, CDC, time-series, log analysis, real-time warehouse, point query, data platform, or any scenario where table design decisions are being made. Also triggers on replacing or migrating from legacy analytics/search/serving stacks such as Impala, Kudu, Elasticsearch/ES, Greenplum, Presto, HBase, Hive, Hadoop, Redis, or Lambda-style multi-engine data platforms, even when Apache Doris is not named explicitly. Also use when user provides an Apache Doris connection string or asks to get started. Also triggers on slow query investigation, query profiling, runtime performance diagnosis, tablet skew analysis, and table health checks — any scenario where runtime evidence (profile output, tablet distribution) informs optimization. Cluster lifecycle, billing, and networking are managed-service operations, out of scope here — use your platform's cluster-management console for those.
SKILL.md
Apache Doris Best Practices
Problem-first table design intelligence for Apache Doris. 37 rules, 7 use case templates, 4 sizing guides. All details in
references/directory.
1 ▸ Problem-First Routing
I need to build…
| Problem | Template(s) | Key Rules |
|---|---|---|
| Real-time log/event analytics | usecase-log-event | DUPLICATE, RANGE partition, dynamic TTL, ZSTD |
| CDC / MySQL sync to Doris | usecase-cdc-sync | UNIQUE MoW, sequence_col, HASH bucket |
| Dashboard with pre-aggregated metrics | usecase-dashboard-metrics | AGGREGATE, BITMAP_UNION, sync MV |
| User-facing API with low-latency point queries | usecase-point-query | UNIQUE MoW, store_row_column, BloomFilter |
| Star schema with JOIN-heavy analytics | usecase-star-schema-join | Colocation, same bucket key/count |
| Small dimension / lookup table | usecase-dimension-lookup | DUPLICATE, RANDOM bucket, 3 buckets |
| Observability (logs + traces + metrics) | usecase-observability | 3 tables: DUP logs, DUP traces, AGG metrics |
| Vehicle/fleet tracking | usecase-log-event + usecase-point-query | Time-series + point-query hybrid |
| E-commerce order analytics | usecase-star-schema-join + usecase-dashboard-metrics | Star schema + AGG rollups |
| Full-text search / content search | schema-index-text-search | Inverted index, MATCH, BM25 |
| User behavior / funnel analysis | schema-types-bitmap-count-distinct | BITMAP_UNION, bitmap_intersect |
| Semi-structured JSON data | schema-types-variant-json | VARIANT type, schema_template |
My query is slow after evidence shows…
For live slow-query or runtime diagnosis, do not use this table as the first response. First read references/cli-investigation.md and collect or attempt evidence (profile get, profile list, profile history, tablet, EXPLAIN, or auth status). Use this table only after evidence points to the symptom.
| Symptom | Check These Rules | Quick Fix |
|---|---|---|
| Full table scan on WHERE clause | schema-keys-selectivity-first | Move filtered column to sort key position 1 |
| JOINs are slow / shuffle | usecase-star-schema-join | Small dims (<1GB): broadcast + runtime filter. Large: colocation |
| COUNT DISTINCT is slow | schema-types-bitmap-count-distinct | Switch to BITMAP_UNION aggregation |
| LIKE '%keyword%' is slow | schema-index-ngram-for-like | Add NGram BloomFilter index |
| Point query latency too high | usecase-point-query | Enable store_row_column + Prepared Statement |
| Storage growing too fast | schema-partition-auto-on-demand + schema-props-compression | AUTO PARTITION + ZSTD compression + scheduled DROP PARTITION |
| Sync MV not being used | schema-mv-sync-rollup | Use raw columns (not date_trunc) in MV GROUP BY; unique aliases |
| Async MV rewrite fails | schema-mv-async-join + schema-mv-async-limits | Check State/RefreshState; query MV directly if predicate fails |
| Data skew / hot tablets | schema-bucket-composite-for-skew | Composite bucket key or RANDOM |
| Import fails / data version error | schema-mv-async-limits | Check concurrent MV refresh limit (max 3) |
| VARCHAR in key kills perf | schema-keys-fixed-length-types | Move VARCHAR after fixed-length types |
| Writes slow on UNIQUE table | schema-model-prefer-mow | Ensure MoW is enabled (not MoR) |
2 ▸ Pre-Flight Checklist (Before Any CREATE TABLE)
Run through this checklist in order. Each step references the relevant rule:
- Data model — UNIQUE (updates?) vs DUPLICATE (append?) vs AGGREGATE (pre-agg only?) →
schema-model-choose-for-workload - Partition strategy — Time-series? AUTO PARTITION preferred. Small table? Skip. Do NOT combine AUTO with dynamic_partition. →
schema-partition-* - Bucket key + count — HASH on JOIN key. Calculate explicit count:
daily_GB / target_tablet_GB. Use explicit fallback counts when volume is unknown: 3 for small dimensions, 8 for medium tables, 16-32 for large daily fact tables. →schema-bucket-* - Sort key order — High-selectivity first, fixed-length before VARCHAR →
schema-keys-* - Data types — Native types, not STRING. DECIMAL not FLOAT. →
schema-types-* - Indexes — BloomFilter for equality, Inverted for text, NGram for LIKE →
schema-index-* - Properties — MoW enabled? Compression? Cloud mode replication_num=1? →
schema-props-* - DDL hard constraints (Apache Doris rejects DDL if any violated):
- UNIQUE KEY + PARTITION BY RANGE → partition column MUST be in the UNIQUE KEY:
UNIQUE KEY(id, dt) PARTITION BY RANGE(dt) - Key columns must be the FIRST N columns in schema, same order — put key cols first, non-key after. Example:
UNIQUE KEY(account_id, symbol)means schema must start withaccount_id, symbol, ...— never place non-key columns between key columns store_row_column = "true"works on UNIQUE MoW and DUPLICATE — NOT on AGGREGATE (Doris rejects AGG: "Aggregate table can't support row column"). Verified on 4.x; older versions were UNIQUE-only- AUTO PARTITION requires
date_trunc()AND empty parens:AUTO PARTITION BY RANGE(date_trunc(col, 'day')) ()— bare column name fails, missing()fails - Dynamic partition requires explicit
PARTITION BY RANGE(col) ()clause in DDL — properties alone are not enough - Do not set
dynamic_partition.buckets; put the numeric count only inDISTRIBUTED BY HASH(col) BUCKETS N compaction_policy = "time_series"only for DUPLICATE tables — fails on UNIQUE- Async MV refresh: use
REFRESH AUTO ON SCHEDULE EVERY 10 MINUTEorREFRESH COMPLETE ON SCHEDULE EVERY 10 MINUTE— NOTREFRESH SCHEDULE EVERY, NOTREFRESH ASYNC EVERY(INTERVAL ...). Minimum interval: 1 MINUTE - MV using
NOW()/CURDATE(): addPROPERTIES ("enable_nondeterministic_function" = "true") - BOOLEAN defaults must be quoted:
DEFAULT "true"notDEFAULT TRUE - BloomFilter index: use
PROPERTIES ("bloom_filter_columns" = "col1,col2")— NOT inlineINDEX ... USING BLOOM FILTER - AGGREGATE column syntax: aggregation function BEFORE default:
col BIGINT SUM DEFAULT "0"— NOTcol BIGINT DEFAULT "0" SUM - AGGREGATE
DEFAULT "null"only works for VARCHAR — fails on INT, DATE, DECIMAL, BIGINT. Omit DEFAULT entirely for REPLACE_IF_NOT_NULL on non-string types:vip_level INT REPLACE_IF_NOT_NULL(notDEFAULT "null") enable_unique_key_partial_updateis a session variable, NOT a table property- Full details:
schema-ddl-gotchas
- UNIQUE KEY + PARTITION BY RANGE → partition column MUST be in the UNIQUE KEY:
2b ▸ DDL Templates (copy the closest match, customize columns)
For each CREATE TABLE, select the closest template below. Customize column names, types, bucket count, and partition settings. Do NOT write DDL from scratch.
T1: Append-only events/logs (DUPLICATE)
CREATE TABLE events (
entity_id VARCHAR(64) NOT NULL,
event_time DATETIME NOT NULL,
event_type VARCHAR(50) NOT NULL,
payload VARIANT
) DUPLICATE KEY(entity_id, event_time, event_type)
PARTITION BY RANGE(event_time) ()
DISTRIBUTED BY HASH(entity_id) BUCKETS 10
PROPERTIES (
"dynamic_partition.enable" = "true",
"dynamic_partition.time_unit" = "DAY",
"dynamic_partition.start" = "-90",
"dynamic_partition.end" = "3",
"dynamic_partition.prefix" = "p",
"compression" = "zstd",
"compaction_policy" = "time_series",
"replication_num" = "1"
);
T2: Updatable with partition (UNIQUE MoW + CDC)
CREATE TABLE orders (
order_id BIGINT NOT NULL,
order_time DATETIME NOT NULL,
update_time DATETIME NOT NULL,
status VARCHAR(20),
amount DECIMAL(18,2)
) UNIQUE KEY(order_id, order_time)
PARTITION BY RANGE(order_time) ()
DISTRIBUTED BY HASH(order_id) BUCKETS 5
PROPERTIES (
"enable_unique_key_merge_on_write" = "true",
"function_column.sequence_col" = "update_time",
"dynamic_partition.enable" = "true",
"dynamic_partition.time_unit" = "DAY",
"dynamic_partition.start" = "-365",
"dynamic_partition.end" = "3",
"dynamic_partition.prefix" = "p",
"replication_num" = "1"
);
T3: Small dimension / lookup (UNIQUE, no partition)
CREATE TABLE dim_product (
product_id INT NOT NULL,
name VARCHAR(200),
category VARCHAR(50)
) UNIQUE KEY(product_id)
DISTRIBUTED BY HASH(product_id) BUCKETS 3
PROPERTIES (
"enable_unique_key_merge_on_write" = "true",
"replication_num" = "1"
);
T4: Pre-aggregated KPIs (AGGREGATE)
CREATE TABLE daily_kpi (
stat_date DATE NOT NULL,
dimension VARCHAR(50) NOT NULL,
metric_sum BIGINT SUM DEFAULT "0",
metric_max DOUBLE MAX DEFAULT "0",
unique_users BITMAP BITMAP_UNION
) AGGREGATE KEY(stat_date, dimension)
PARTITION BY RANGE(stat_date) ()
DISTRIBUTED BY HASH(dimension) BUCKETS 3
PROPERTIES (
"dynamic_partition.enable" = "true",
"dynamic_partition.time_unit" = "MONTH",
"dynamic_partition.start" = "-12",
"dynamic_partition.end" = "1",
"dynamic_partition.prefix" = "p",
"replication_num" = "1"
);
T5: Point query / API serving (UNIQUE MoW + row store)
CREATE TABLE user_profiles (
user_id BIGINT NOT NULL,
update_time DATETIME NOT NULL,
name VARCHAR(100),
data VARIANT
) UNIQUE KEY(user_id)
DISTRIBUTED BY HASH(user_id) BUCKETS 5
PROPERTIES (
"enable_unique_key_merge_on_write" = "true",
"function_column.sequence_col" = "update_time",
"store_row_column" = "true",
"light_schema_change" = "true",
"replication_num" = "1"
);
3 ▸ Connection & CLI
Apache Doris speaks the MySQL protocol, so the always-available path is any MySQL-compatible client (mysql) plus SQL and the FE HTTP REST API. Some distributions also ship an optional management CLI (referred to here as doriscli) that adds ergonomic profiling and diagnostics commands — use it when your distribution provides one, otherwise use the native path.
Detect the optional CLI
Before running any queries, detect whether the CLI binary is available:
- Check
DORIS_CLI_PATHenv var — if set, use that binary path command -v doriscli— use from PATH- If none available: fall back to
mysqlclient (seereferences/start-*.md)
When doriscli is available, prefer it for all operations:
| Task | doriscli Command |
|---|---|
| Run SQL | doriscli sql "SELECT ..." |
| DDL inspection | doriscli sql "SHOW CREATE TABLE db.t" |
| Table/tablet health | doriscli tablet db.t (overview) or doriscli tablet db.t --detail |
| Profile a slow query | doriscli sql "SELECT ..." --profile → captures query_id |
| Get query profile | doriscli profile get <qid> or --full for complete diagnosis |
| Compare fast vs slow | doriscli profile diff <slow_qid> <fast_qid> |
| Performance trend | doriscli profile history <sql_pattern> --days 7 |
| Test connection | doriscli auth status |
| Switch environment | doriscli use <name> |
Runtime Query Investigation
For slow queries or runtime performance issues, read references/cli-investigation.md.
- Evidence first is mandatory: collect or attempt profile, tablet, DDL, stats, EXPLAIN, history, active-query, or connection evidence before forming hypotheses. If evidence cannot be collected locally, state that and provide the exact commands to run
- Prefer existing profiles: use
profile get <query_id>,profile list, orprofile historybefore re-executing SQL - Proactive discovery: for vague slow-query reports, start with
auth status,profile list --active, and recentprofile listbefore asking the user for more context - Safety gate: before running user SQL with
--profile, check whether it is safe (no DDL, no mutation, no unbounded scan). For unknown, peak-hour, or expensive SQL, rundoriscli sql "EXPLAIN <query>" --format jsonfirst and ask confirmation or request an existing query_id - Hypotheses, not verdicts: diagnostic mappings are heuristics. Present evidence, likely cause, what to check next, and when the conclusion may be wrong
- If doriscli is unavailable, fall back to SQL commands listed in the reference
- Always use
--format jsonfor structured agent-readable output
Quick-start guides
references/start-self-hosted.md— Self-hosted / BYOC / on-prem- Cloud mode (storage-compute) connection differs only in the HTTP port (8080 vs 8030) — same guide applies
4 ▸ Cluster Sizing
Sizing guides are in:
references/sizing-fe.md— FE node sizingreferences/sizing-be-integrated.md— BE sizing (integrated storage)references/sizing-be-cloud.md— BE sizing (cloud / storage-compute)references/sizing-storage-formula.md— Storage calculation formula
5 ▸ Rule Index by Category
Data Model — CRITICAL (4 rules)
schema-model-choose-for-workload— DUP vs UNIQUE vs AGG decision treeschema-model-prefer-mow— Always MoW for UNIQUE tablesschema-model-avoid-agg-for-updates— AGG cannot UPDATE/DELETEschema-model-sequence-col-for-cdc— Sequence column for out-of-order CDC
Partition Strategy — CRITICAL (4 rules)
schema-partition-range-for-timeseries— RANGE for time-seriesschema-partition-dynamic-ttl— Dynamic partition for automated TTLschema-partition-auto-on-demand— AUTO for sporadic dataschema-partition-skip-for-small— Skip partitioning under 1 GB
Bucket Strategy — CRITICAL (5 rules)
schema-bucket-hash-vs-random— HASH for pruning, RANDOM for DUP onlyschema-bucket-high-cardinality-key— Choose high-cardinality columnschema-bucket-composite-for-skew— Composite key to fix data skewschema-bucket-target-size— Target 1-10 GB per tabletschema-bucket-cloud-mandatory-hash— Cloud MoW requires HASH
Sort Key — CRITICAL (5 rules)
schema-keys-selectivity-first— High selectivity firstschema-keys-fixed-length-types— Fixed-length before VARCHARschema-keys-prefix-index-limits— 36 bytes max, VARCHAR terminates itschema-keys-cluster-key-for-mow— Cluster key for UNIQUE tablesschema-keys-avoid-float— No FLOAT/DOUBLE in sort key
Data Types — HIGH (5 rules)
schema-types-native-vs-string— Native types, not STRINGschema-types-zonemap-limitations— JSON/ARRAY disable ZoneMapschema-types-variant-json— VARIANT for semi-structured JSONschema-types-bitmap-count-distinct— BITMAP_UNION for exact count-distinctschema-types-doris-specifics— DATETIME precision, VARCHAR vs STRING
Indexes — HIGH (7 rules)
schema-index-bloomfilter— BloomFilter for equalityschema-index-inverted— Inverted for text/rangeschema-index-ngram-for-like— NGram for LIKE %pattern%schema-index-bitmap— Bitmap for medium cardinalityschema-index-vector— HNSW/IVF for ANN searchschema-index-text-search— Full-text MATCH + BM25
Query Acceleration — HIGH (3 rules)
schema-mv-sync-rollup— Sync MV for single-table aggregationschema-mv-async-join— Async MV for multi-table JOINschema-mv-async-limits— Operational limits (50M rows, 3 concurrent)
Table Properties — HIGH/MEDIUM (2 rules)
schema-props-cloud-forced— Cloud mode forced propertiesschema-props-compression— LZ4 vs ZSTD compression
Caching — MEDIUM (2 rules)
schema-cache-file-cache— File cache for cloud modeschema-cache-query-partition— Query and partition cache
More skills from the doris-skills repository
View all 14 skillsdoris-architecture-advisor
design Apache Doris data architectures
Jul 12ArchitectureData EngineeringData PipelineDatabasedoris-debug
diagnose Apache Doris production issues
Jul 25DebuggingMonitoringOperationsdoris-debug-cloud
debug Apache Doris cloud mode issues
Jul 25CloudDebuggingPerformancedoris-debug-compaction
resolve Apache Doris compaction lag
Jul 25DatabaseDebuggingPerformancedoris-debug-data-lake
debug Apache Doris data lake connectivity
Jul 25Data EngineeringDatabaseDebuggingdoris-debug-deployment
troubleshoot Apache Doris deployment failures
Jul 25DebuggingDeploymentOperations
More from Apache Software Foundation
View publisherdatafusion-python
write Apache DataFusion Python code
datafusion-python
Jul 12Data AnalysisPythonSQLbydbql
generate and execute BanyanDB BydbQL queries
skywalking-banyandb
Jul 12AnalyticsDatabaseSQLcompiling
compile and build BanyanDB projects
skywalking-banyandb
Jul 12BuildEngineeringgh-pull-request
create GitHub pull requests for BanyanDB
skywalking-banyandb
Jul 12GitHubPull Requestsvendor-update
update Go and Node.js vendor dependencies
skywalking-banyandb
Jul 12GoNode.jscayenne-cgen
generate Cayenne entity Java classes
cayenne
Jul 12Data ModelingJavaORM