analytics-scala

name	analytics-scala
summary	Couchbase Analytics Service (CBAS) for Scala — run OLAP queries, window functions, and external data links without impacting operational workloads (Server 6.x–7.x; use columnar-analytics for Server 8+)
description	Couchbase Analytics Service (CBAS) for Scala — run OLAP queries, window functions, and external data links without impacting operational workloads (Server 6.x–7.x; use columnar-analytics for Server 8+)
compatibility	Scala SDK 1.x. Requires couchbase-scala-client>=1.0.
metadata	{"last_verified":"2026-05","min_server_version":"6.0","deprecated_by":"columnar-analytics","deprecated_since":"8.0","max_server_version":"7.6","handoff":[{"condition":"user asks about SQL++ queries","skill":"server-querying-scala"},{"condition":"user is upgrading to Server 8.x and wants the columnar engine","skill":"columnar-analytics"},{"condition":"user wants to write results back to operational collections transactionally","type":"variant","skill":"transactions-scala"}]}

Couchbase Analytics — Scala

Version disambiguation: This skill covers Couchbase Server 6.x–7.x (legacy CBAS). If the user mentions Server 8, Couchbase 8, or the columnar engine, route to columnar-analytics instead. If the server version is unknown, ask before proceeding. Analytics runs OLAP queries on a shadow copy of your data, isolated from KV and Query Service workloads. Use it for aggregations, reports, and joins across large datasets.

---

Setup — Create an Analytics Collection

-- Link the default bucket (once, in the Analytics workbench or cbsh)
CREATE DATAVERSE myapp IF NOT EXISTS;
USE myapp;
CREATE ANALYTICS COLLECTION orders ON `myapp`.`_default`.`orders` IF NOT EXISTS;

---

Running Analytics Queries

The Scala SDK uses cluster.analyticsQuery() — the same API shape as cluster.query().

import com.couchbase.client.scala._
import com.couchbase.client.scala.analytics.AnalyticsOptions

val cluster = Cluster.connect("couchbase://localhost",
  sys.env("CB_USERNAME"), sys.env("CB_PASSWORD")).get

// Simple aggregation
val result = cluster.analyticsQuery(
  "SELECT country, COUNT(*) AS cnt FROM orders GROUP BY country ORDER BY cnt DESC"
).get

result.rowsAs[String].get.foreach(println)

Parameterized queries

val result = cluster.analyticsQuery(
  "SELECT * FROM orders WHERE country = $country",
  AnalyticsOptions().parameters(Map("country" -> "US"))
).get

Window functions

val result = cluster.analyticsQuery("""
  SELECT name, country, stars,
         RANK() OVER (PARTITION BY country ORDER BY stars DESC) AS rank
  FROM hotels
""").get

---

Key differences from operational SQL++

	Analytics (analyticsQuery)	Operational (query)
Data source	Shadow copy (async replicated)	Live data
Isolation	No impact on KV/Query	Shares Query Service resources
Consistency	Eventually consistent	Configurable (request_plus)
Use case	OLAP, reports, large joins	OLTP, low-latency lookups

Analytics vs Query Service: See shared/server/analytics-vs-query.md for the full comparison, setup steps, and common errors.

---

Writing Results Back

Use transactions for atomic write-back, or individual KV upserts for non-atomic bulk writes.

val result = cluster.analyticsQuery(
  """SELECT id, total FROM orders WHERE status = "complete""""
).get

val collection = cluster.bucket("myapp").defaultCollection()

result.rowsAs[JsonObject].get.foreach { row =>
  collection.upsert(s"summary::${row.str("id")}",
    JsonObject.create.put("total", row.dbl("total"))).get
}

For atomic multi-document write-back, wrap in a transaction — see transactions-scala.

Troubleshooting

Error	Cause	Fix
DataverseNotFoundException	Analytics collection not created	Run CREATE ANALYTICS COLLECTION
CompilationException	SQL++ syntax error	Check field names and dataverse
Slow query	Missing index or large dataset	Add Analytics index or filter earlier