Category: Database

One of Oracle 23ai’s headline features is JSON Relational Duality Views. The concept is elegant: define a view once, and it automatically presents the same data as both structured relational tables and JSON documents. No duplication, no synchronization lag, no ETL.

Creating a duality view:

CREATE OR REPLACE JSON RELATIONAL DUALITY VIEW order_dv AS
SELECT JSON {
    '_id'       : o.order_id,
    'status'    : o.status,
    'customer'  : o.customer_name,
    'items'     : [
        SELECT JSON {
            'product'  : oi.product_name,
            'qty'      : oi.quantity,
            'price'    : oi.unit_price
        }
        FROM order_items oi
        WHERE oi.order_id = o.order_id
    ]
}
FROM orders o WITH INSERT UPDATE DELETE;

Reading as JSON:

SELECT data FROM order_dv WHERE data.status = 'PENDING';

Reading as relational (same data):

SELECT * FROM orders WHERE status = 'PENDING';

Both queries hit the same underlying tables. No duplication.

Why this is architecturally significant

Modern applications often have mixed workloads. A REST API might need JSON documents. Analytics queries need relational structure. Traditional solutions involve maintaining both a relational database and a document store (like MongoDB) with synchronization middleware.

Duality Views eliminate that. The same Oracle database, the same tables, the same transaction — but surfaced as JSON for your Node.js app and as SQL for your BI tool.

ORDS integration: Oracle REST Data Services (ORDS) can automatically expose duality views as REST endpoints, letting you build MongoDB-style document APIs on top of relational data with minimal code.

This is a genuinely novel architecture that deserves its own blog series. For now, the key point: if you have applications that need document APIs and relational reporting, 23ai’s duality views are worth evaluating seriously.

Managing object-level privileges in Oracle has always been granular — sometimes too granular. If you wanted a user to have SELECT on every table in a schema, you had to grant it table by table. Add a new table later? Grant again. Automate it? Write a script.

Oracle 23ai introduces schema-level privileges — the ability to grant a privilege on an entire schema with a single statement that automatically applies to all current and future objects in that schema.

Granting schema-level SELECT:

GRANT SELECT ANY TABLE ON SCHEMA hr TO reporting_user;

reporting_user can now SELECT from any table (current or future) in the hr schema. No more individual grants per table.

Other supported schema-level privileges:

GRANT INSERT ANY TABLE   ON SCHEMA app_data TO etl_user;
GRANT UPDATE ANY TABLE   ON SCHEMA app_data TO etl_user;
GRANT DELETE ANY TABLE   ON SCHEMA app_data TO etl_user;
GRANT EXECUTE ANY PROCEDURE ON SCHEMA hr   TO app_user;
GRANT SELECT ANY SEQUENCE   ON SCHEMA hr   TO app_user;

Revoking schema-level privileges:

REVOKE SELECT ANY TABLE ON SCHEMA hr FROM reporting_user;

Key behavior: Schema privileges are dynamic. When a new table is created in hr, reporting_user immediately has SELECT on it — no additional grant required.

Checking existing schema-level grants:

SELECT grantee, privilege, schema
FROM   dba_schema_privs
ORDER BY schema, grantee;

Real-world impact

This dramatically simplifies privilege management for read-only reporting users, ETL processes, and microservice accounts that need access to a full schema rather than individual tables. It’s a long-requested feature that reduces the DBA overhead of schema management and eliminates entire categories of “who forgot to grant this?” support tickets.

Oracle 23ai adds a meaningful set of new aggregate functions to SQL. These fill gaps in analytical and statistical capabilities that previously required PL/SQL, external tools, or verbose workarounds.

CHECKSUM

Returns a deterministic checksum value for a set of rows. Extremely useful for data validation and change detection.

SELECT CHECKSUM(*) AS row_fingerprint
FROM   employees
WHERE  department_id = 10;

If any row in the result set changes, the checksum changes — making it ideal for comparing snapshots.

BIT_AND_AGG / BIT_OR_AGG / BIT_XOR_AGG

Bitwise aggregate functions — apply bitwise AND, OR, or XOR across all values in a group.

-- Which permissions are held by ALL members of a group?
SELECT role_id, BIT_AND_AGG(permission_flags) AS common_permissions
FROM   role_members
GROUP BY role_id;

-- Which permissions are held by AT LEAST ONE member?
SELECT role_id, BIT_OR_AGG(permission_flags) AS any_permission
FROM   role_members
GROUP BY role_id;

SKEWNESS_POP / SKEWNESS_SAMP

Measures statistical skewness of a distribution — how asymmetric the data is around the mean.

SELECT SKEWNESS_POP(salary) AS salary_skew
FROM   employees;

A positive value means the distribution tails right (a few very high salaries); negative means it tails left.

KURTOSIS_POP / KURTOSIS_SAMP

Measures the “tailedness” of a distribution — whether outliers are common or rare.

SELECT KURTOSIS_SAMP(response_time_ms) AS response_time_kurtosis
FROM   api_logs;

Why these matter

Before 23ai, these calculations required either loading data into R/Python, implementing them manually as complex SQL expressions, or using the DBMS_STAT_FUNCS package. Now they’re first-class aggregate functions available anywhere SQL is valid — in reports, views, materialized views, and analytics queries.

Here’s one of my favorite SQL improvements in Oracle 23ai, and one with a personal history I’ll share more about in September: GROUP BY ALL.

Every developer who has written aggregate queries knows the pain. You write a SELECT with some aggregate functions (SUM, COUNT, AVG), and Oracle forces you to list every non-aggregated column in the GROUP BY clause. Add a column to the SELECT? Add it to GROUP BY too. Forget one? ORA-00979: not a GROUP BY expression.

GROUP BY ALL solves this automatically. It groups by every non-aggregate expression in the SELECT list.

Before (Oracle ≤ 19c / 21c):

SELECT department_id, department_name, job_id, location_id,
       COUNT(*) AS headcount,
       AVG(salary) AS avg_salary
FROM   employees
GROUP BY department_id, department_name, job_id, location_id;

After (Oracle 23ai):

SELECT department_id, department_name, job_id, location_id,
       COUNT(*) AS headcount,
       AVG(salary) AS avg_salary
FROM   employees
GROUP BY ALL;

Oracle automatically groups by department_id, department_name, job_id, and location_id — every column that isn’t inside an aggregate function.

Adding a new column? No GROUP BY change needed:

SELECT department_id, department_name, job_id, location_id, country_id,
       COUNT(*) AS headcount,
       AVG(salary) AS avg_salary
FROM   employees
GROUP BY ALL;  -- country_id is automatically included

Works with ROLLUP, CUBE, and GROUPING SETS too:

SELECT department_id, job_id,
       SUM(salary)
FROM   employees
GROUP BY ROLLUP ALL;

This feature is one of those “why didn’t this always exist?” improvements. Less boilerplate, fewer bugs from missing a column in GROUP BY, and queries that are easier to extend. I’ll have more to say about the origins of this idea in September’s special series.

Alongside the UPDATE improvement covered last week, Oracle 23ai also brings direct JOIN support to DELETE statements. The same ergonomic benefit applies — no more correlated subqueries just to filter a delete based on another table.

The old way:

DELETE FROM order_items oi
WHERE oi.order_id IN (
    SELECT o.order_id
    FROM   orders o
    WHERE  o.status = 'CANCELLED'
    AND    o.order_date < DATE '2024-01-01'
);

The new way (Oracle 23ai):

DELETE FROM order_items oi
JOIN   orders o ON o.order_id = oi.order_id
WHERE  o.status = 'CANCELLED'
AND    o.order_date < DATE '2024-01-01';

Same result, but the relationship between the two tables is explicit and readable.

Cascade delete pattern:

-- Delete all line items for expired promotions
DELETE FROM promotion_items pi
JOIN   promotions p ON p.promo_id = pi.promo_id
WHERE  p.expiry_date < SYSDATE;

An important caveat: The join conditions determine which rows are deleted from the target table. You cannot delete from the joined table — only from the primary table in the DELETE FROM clause. If you try to delete from both sides, Oracle will raise an error.

Why this matters

In data warehousing and ETL scenarios, conditional deletes based on staging table content are extremely common. Before 23ai, you had to choose between a readable EXISTS subquery or a sometimes-faster IN subquery, both of which hide the join relationship.

The direct JOIN syntax is not only cleaner to write — it’s also easier to review in code reviews and easier to optimize because the join relationship is explicit in the query structure.

Updating rows in one table based on data from another has always been possible in Oracle, but the syntax was never elegant. You had to use a correlated subquery or an inline view workaround. Oracle 23ai introduces direct JOIN syntax in UPDATE statements, making multi-table updates far more readable.

The old way — correlated subquery:

UPDATE employees e
SET    e.department_name = (
           SELECT d.department_name
           FROM   departments d
           WHERE  d.department_id = e.department_id
       )
WHERE EXISTS (
    SELECT 1 FROM departments d
    WHERE  d.department_id = e.department_id
    AND    d.active = 1
);

The new way — direct JOIN (Oracle 23ai):

UPDATE employees e
JOIN   departments d ON d.department_id = e.department_id
SET    e.department_name = d.department_name
WHERE  d.active = 1;

Much cleaner. The intent is immediately clear — join employees to departments, update the column, filter by a condition on the joined table.

Supported join types:

• INNER JOIN
• LEFT JOIN
• RIGHT JOIN

Multi-column update example:

UPDATE employees e
JOIN   salary_bands sb ON sb.grade = e.pay_grade
SET    e.min_salary = sb.min_salary,
       e.max_salary = sb.max_salary
WHERE  sb.effective_date = DATE '2025-01-01';

Performance note: The Oracle optimizer can leverage indexes and statistics on the joined table just as it does with regular SELECT joins. In many cases, this will be equivalent or better than the correlated subquery approach.

If you’ve ever looked at UPDATE statements and wished they read more like SELECT statements, Oracle 23ai has finally granted that wish. This is one of those features you didn’t know you were missing until you have it.

Oracle 23ai introduces table value constructors — a way to use a VALUES clause directly in a FROM clause, creating an inline result set without needing a real table, a UNION ALL chain, or a PL/SQL collection.

This is especially useful for testing queries, building lookup sets, or constructing small datasets on the fly.

Basic syntax:

SELECT *
FROM   (VALUES (1, 'Alice'), (2, 'Bob'), (3, 'Carol'))
       AS t(id, name);

Practical use case — joining against a small inline dataset:

SELECT e.employee_name, r.region_label
FROM   employees e
JOIN   (VALUES ('EMEA', 'Europe, Middle East & Africa'),
               ('APAC', 'Asia Pacific'),
               ('AMER', 'Americas'))
       AS r(region_code, region_label)
ON     e.region = r.region_code;

Why not just use DUAL with UNION ALL?

Before 23ai, the standard Oracle workaround was:

SELECT 1 AS id, 'Alice' AS name FROM DUAL
UNION ALL
SELECT 2, 'Bob'   FROM DUAL
UNION ALL
SELECT 3, 'Carol' FROM DUAL;

It works, but it’s verbose and scales poorly. With FROM VALUES, the syntax is cleaner and performs better because Oracle can optimize it as a single operation.

Testing aggregate functions inline:

SELECT region, SUM(sales) AS total
FROM   (VALUES ('EMEA', 500), ('EMEA', 300), ('APAC', 800))
       AS t(region, sales)
GROUP BY region;

This pattern is fantastic for unit-testing SQL logic without needing real data in real tables. Write your query against a known dataset, validate it, then swap in the real table.

Table value constructors are a mature feature in ANSI SQL and in most other major databases. Their arrival in Oracle 23ai is welcome — and makes a lot of common SQL patterns significantly cleaner.

Oracle DBAs have a long and complicated relationship with DUAL. This single-row, single-column table has been the answer to a quirky limitation for decades: Oracle SQL required a FROM clause on every SELECT statement, even when you just wanted to evaluate an expression.

SELECT SYSDATE FROM DUAL;
SELECT 1 + 1 FROM DUAL;
SELECT SYS_GUID() FROM DUAL;

In every other major database — PostgreSQL, MySQL, SQL Server, SQLite — you can simply write:

SELECT SYSDATE;
SELECT 1 + 1;

With Oracle 23ai, this finally works in Oracle too. The FROM clause is now optional when no table data is needed.

Before (still valid, still works):

SELECT SYSDATE FROM DUAL;
SELECT USER FROM DUAL;
SELECT 100 * 1.08 FROM DUAL;

After (23ai):

SELECT SYSDATE;
SELECT USER;
SELECT 100 * 1.08;

Why does this matter?

Mostly for ergonomics and cross-database compatibility. If you’re writing SQL that needs to run across multiple databases, removing the FROM DUAL dependency makes your code more portable. It also reduces onboarding friction for developers coming from other database backgrounds who find DUAL confusing.

DUAL itself isn’t going anywhere — Oracle has committed to maintaining backward compatibility, and millions of existing queries reference it. But new code no longer needs it for expression evaluation.

This is a small quality-of-life improvement, but it’s the kind that makes you realize Oracle is finally listening more closely to developer ergonomics. Small changes compound. And as we’ll see throughout this series, 23ai has many of them.

Every DBA has seen it: a deployment script fails halfway through because a table already exists, or a DROP command fails because the object isn’t there. The usual workaround was wrapping DDL in PL/SQL exception blocks or using DBMS_METADATA tricks. Not anymore.

Oracle 23ai introduces IF [NOT] EXISTS syntax for DDL statements, bringing Oracle in line with PostgreSQL, MySQL, and SQL Server.

Creating objects safely:

CREATE TABLE employees (
    id     NUMBER,
    name   VARCHAR2(100)
) IF NOT EXISTS;

If the table already exists, Oracle simply skips the command — no error, no exception, no rollback. Clean and predictable.

Dropping objects safely:

DROP TABLE temp_staging IF EXISTS;
DROP INDEX idx_emp_name IF EXISTS;
DROP SEQUENCE seq_invoice IF EXISTS;

No more ORA-00942: table or view does not exist blowing up your scripts.

Supported objects include: TABLE, VIEW, INDEX, SEQUENCE, TRIGGER, PROCEDURE, FUNCTION, PACKAGE, TYPE, and more.

Why this matters for real deployments

If you manage database migrations with tools like Flyway or Liquibase, or if you run repeatable deployment scripts across multiple environments (dev, QA, prod), this feature is a genuine time-saver. You can now write idempotent DDL scripts natively in SQL without PL/SQL wrappers.

Before 23ai, a typical safe-drop pattern looked like:

BEGIN
  EXECUTE IMMEDIATE 'DROP TABLE temp_staging';
EXCEPTION
  WHEN OTHERS THEN NULL;
END;
/

Now it’s just:

DROP TABLE temp_staging IF EXISTS;

One line. No PL/SQL block. No hidden WHEN OTHERS THEN NULL swallowing real errors.

This is a small feature with a big impact on daily DBA work. If your team manages schema migrations, start using IF [NOT] EXISTS immediately.

If you’ve worked with PL/SQL, you’ve always had access to BOOLEAN. But in Oracle SQL — in actual table columns, queries, and DML — it simply didn’t exist. You had to emulate it with NUMBER(1), CHAR(1) with check constraints, or VARCHAR2 with values like 'Y'/'N'. Each team had its own convention. None were truly boolean.

Oracle 23ai ends this inconsistency. BOOLEAN is now a native SQL data type.

Defining a boolean column:

CREATE TABLE feature_flags (
    feature_name  VARCHAR2(100),
    is_enabled    BOOLEAN DEFAULT FALSE NOT NULL,
    is_beta       BOOLEAN DEFAULT TRUE
);

Inserting and querying boolean data:

INSERT INTO feature_flags VALUES ('dark_mode', TRUE, FALSE);

SELECT feature_name
FROM   feature_flags
WHERE  is_enabled = TRUE;

Accepted literal values:

Oracle is flexible about what it accepts as boolean input. All of the following work:

• TRUE / FALSE
• 'YES' / 'NO'
• 'ON' / 'OFF'
• '1' / '0'
• 1 / 0

JSON integration

One of the biggest wins is JSON interoperability. Oracle’s JSON functions now map SQL BOOLEAN columns directly to JSON true/false — no more "is_active": "Y" in your API responses.

Migration note

If you have existing columns using NUMBER(1) or CHAR(1) as boolean proxies, you’ll need to handle conversion carefully. Oracle does not auto-cast these to BOOLEAN on column migration. Plan your data migration scripts accordingly.

The BOOLEAN type in SQL is one of those features that seems minor until you realize how much friction it removes. Cleaner DDL, cleaner application code, cleaner JSON output. A long-overdue improvement.