Athena To Redshift

Migrating Queries from Athena to Redshift: Engineering for Scale

Athena might get you moving, but heavy analytics? That’s Redshift territory.

When Athena Hits Its Limits

Interactive analysis over flat Parquet on S3—Athena is purpose-built for that. But as concurrent users jump, data volumes shift past tens of TBs, and costs climb, serverless-on-demand ceases to scale economically. Query latencies also become inconsistent. This is classic: ad-hoc solutions eventually hit an inflection point.

Redshift, with its MPP (massively parallel processing) engine, reserve pricing, and indexing capabilities, enables consistent high throughput on structured, high-value workloads. Migrating is not plug and play—expect to revisit schemas, pipelines, and even business logic.

Athena vs. Redshift: Non-Obvious Contrasts

Note: Redshift Spectrum bridges some gaps, but not always as fast for interactive use.

Reality Check: Migration Steps

Skipping “your mileage may vary.” Here’s a battle-tested pathway:

1. Inventory and Analyze Existing Athena Workloads

• Pull CloudTrail or Athena console history; export query logs for the last 90 days.
• Identify “critical path” queries—ones feeding dashboards, scheduled jobs, or tightly monitored SLAs.
• Note usage of exotic Presto features (e.g., map_agg, lambda ops), which often trip up in Redshift.

Pattern-match for the following:

• Large, repeated full-table scans (e.g., SELECT *)
• Complex UDF chains
• Dependence on open-row single-file structures (*.json or *.csv instead of partitioned Parquet)

Tip: Odd spikes in scanned TBs per query = candidate for deep optimization.

2. Stage Data for Redshift

There are two real options:

• COPY to Cluster: Highest performance, but ETL prep is required.
• Redshift Spectrum: Keep data in S3, define external schemas. Good for cold data, but performance ceiling applies.

Recommended format for loading: Partitioned Parquet with snappy compression. Redshift's COPY command natively ingests this:

COPY prod.events FROM 's3://bucket/events/202406/*.parquet'
IAM_ROLE 'arn:aws:iam::1234567890:role/RedshiftLoadRole'
FORMAT AS PARQUET
REGION 'us-east-1';

Gotcha: if your data is not partitioned by the major query filters (event_date, for instance), loads and queries will lag. Re-partition on S3 if possible.

3. Replatform SQL: From Presto to Redshift

Straight SQL rewrites might work 70% of the time. Watch for function mismatches.

Typical translation snag:

• Presto:

  SELECT cast(json_parse(payload) AS MAP<VARCHAR, VARCHAR>)
  FROM logs
  WHERE cast(payload->'age' AS INTEGER) > 21;
  

• Redshift (No native JSON path queries on Parquet columns):
  • First: ETL JSON into explicit columns on load.
  • Then:

    SELECT age
    FROM logs_clean
    WHERE age > 21;
    

Common changes:

• Presto’s array/map operators are missing in Redshift.
• Window functions: mostly supported, but edge-case syntax (row_number() partitioning) can diverge. Check for silent return of extra rows.
• Date handling: date('2024-06-15') in Presto → plain '2024-06-15' in Redshift. No implicit type coercion, watch for conversion errors.

Non-obvious tip: Default Redshift string functions aren’t as flexible as Presto’s. Bulk query rewrites may need batch refactoring (think: sed + some SQL parse script; tedious but safer than manual changes for >50 queries).

4. Redshift Table Design: Cheap Mistakes to Avoid

• DISTKEY: Collocate frequently joined columns (e.g., user_id in user-session models). But overuse leads to distribution skew.

  CREATE TABLE prod.events (
      user_id BIGINT,
      event_id UUID,
      event_time TIMESTAMP
  )
  DISTKEY(user_id)
  SORTKEY(event_time);
  

• SORTKEY: Time-series? Sort on timestamp. Otherwise, on join keys or major filter columns.
• Encoding: Leave ENCODE AUTO unless dataset is truly massive (20TB+), then test alternatives.

Monitor distribution skew post-load:

SELECT slice, COUNT(*) FROM stv_blocklist GROUP BY slice;

If you see >3x difference between slices, revisit DISTKEY choices.

5. Pipeline Automation: ETL for Prod

• Author Glue (3.0+) jobs, or Airflow DAGs, for periodic extraction.
• Convert raw S3 objects (JSON/CSV) → partitioned Parquet.
• Use manifest files for bulk, append-only loads:

  {
    "entries": [
      {"url":"s3://bucket/events/part-0000.parquet","meta":{}},
      {"url":"s3://bucket/events/part-0001.parquet","meta":{}}
    ]
  }
  

• Redshift COPY ... MANIFEST for multi-file loads.

Practical snag: Redshift tables cannot ingest evolving schema fields as flexibly as Athena on semi-structured. All new columns must be added via ALTER TABLE.

6. Side-by-side Testing and Validation

Run parallel jobs and compare:

• Aggregate counts, sums, min/max per partition/date/key.
• Known issue: Floating-point aggregation can diverge at very large scale due to engine differences.
• For dashboards, reroute a subset of production queries; log user complaints against Redshift latency and data mismatches.

Look for “off by one” or partial row count errors—often due to implicit null handling or timestamp timezone drift.

7. Staged Production Cutover

No need for overnight migrations unless dictated by business pressure.

• Run both systems for 2–6 weeks, let BI teams validate.
• Monitor node CPU/disk on Redshift (stl_alert_event_log, svl_qlog).
• Wrap up old Athena queries once acceptance signoff arrives.

Note: Many teams skip clean Athena deprecation and pay six months of double cost. Set a forced sunset date in advance.

Known Issues & Observations

• Redshift’s VACUUM (table re-clustering) is slow on dense tables. For heavy write/delete workloads, automate VACUUM with CloudWatch alarms.
• “COPY failed due to missing required IAM role” will happen at least once—triple-check roles and cross-account S3 policy permissions.
• Redshift’s connection limits (currently 500/concurrent per cluster, as of RA3.4xl) will cap BI growth unless partitioned across workgroups.
• CloudFormation stack drift for Glue/Redshift integration is a recurring ops overhead—track schema versions outside the templates.

Conclusion

Migrating from Athena to Redshift is a systemic refactor, not a syntax swap. Treat it as a platform transition: rebuild just enough to exploit Redshift’s strengths. Test for side effects: permission oddities, laggy ETL, misprovisioned clusters. Pay attention to query cost models—they’ll dictate your ongoing cloud bill more than you think.

Dev teams who document query translations and ETL structures fare better. Others go in circles mid-migration. Choose your trade-offs and own the outcomes.

Real-world lessons or migration horror stories? Drop a note below. There’s always a surprise hiding in legacy ETL.