Optimization and Best Practices¶

This topic explains some best practices of working with BLUE.

See also our Monitoring Query Performance guide for more information.

Table design¶

This section describes best practices and guidelines for designing tables.

Use date and datetime types for columns¶

When creating tables with dates or timestamps, using the purpose-built DATE and DATETIME types over integer types or TEXT will bring performance and storage footprint improvements, and in many cases huge performance improvements (as well as data integrity benefits). BLUE stores dates and datetimes very efficiently and can strongly optimize queries using these specific types.

Don’t flatten or denormalize data¶

BLUE executes JOIN operations very effectively. It is almost always better to JOIN tables at query-time rather than flatten/denormalize your tables.

This will also reduce storage size and reduce row-lengths.

We highly suggest using INT or BIGINT as join keys, rather than a text/string type.

Use information about the column data to your advantage¶

Knowing the data types and their ranges can help design a better table.

Set `NULL` or `NOT NULL` when relevant¶

For example, if a value can’t be missing (or NULL), specify a NOT NULL constraint on the columns.

Not only does specifying NOT NULL save on data storage, it lets the query compiler know that a column cannot have a NULL value, which can improve query performance.

Sorting¶

Data sorting is an important factor in minimizing storage size and improving query performance.

Minimizing storage saves on physical resources and increases performance by reducing overall disk I/O. Prioritize the sorting of low-cardinality columns. This reduces the number of chunks and extents that BLUE reads during query execution.
Where possible, sort columns with the lowest cardinality first. Avoid sorting TEXT columns with lengths exceeding 50 characters.
For longer-running queries that run on a regular basis, performance can be improved by sorting data based on the WHERE and GROUP BY parameters. Data can be sorted during insert by using Foreign Tables or by using CREATE TABLE AS.

Query best practices¶

This section describes best practices for writing SQL queries.

Reduce data sets before joining tables¶

Reducing the input to a JOIN clause can increase performance. Some queries benefit from retreiving a reduced dataset as a subquery prior to a join.

For example,

SELECT store_name, SUM(amount)
FROM store_dim AS dim INNER JOIN store_fact AS fact ON dim.store_id=fact.store_id
WHERE p_date BETWEEN '2018-07-01' AND '2018-07-31'
GROUP BY 1;

Can be rewritten as

SELECT store_name, sum_amount
FROM store_dim AS dim INNER JOIN
   (SELECT SUM(amount) AS sum_amount, store_id
   FROM store_fact
   WHERE p_date BETWEEN '2018-07-01' AND '2018-07-31'
   group by 2) AS fact
ON dim.store_id=fact.store_id;

Prefer the ANSI JOIN¶

BLUE prefers the ANSI JOIN syntax. In some cases, the ANSI JOIN performs better than the non-ANSI variety.

For example, this ANSI JOIN example will perform better:

ANSI JOIN will perform better¶

SELECT p.name, s.name, c.name
FROM  "Products" AS p
JOIN  "Sales" AS s
  ON  p.product_id = s.sale_id
JOIN  "Customers" as c
  ON  s.c_id = c.id AND c.id = 20301125;

This non-ANSI JOIN is supported, but not recommended:

Non-ANSI JOIN may not perform well¶

SELECT p.name, s.name, c.name
FROM "Products" AS p, "Sales" AS s, "Customers" as c
WHERE p.product_id = s.sale_id
  AND s.c_id = c.id
  AND c.id = 20301125;

Cast smaller types to avoid overflow in aggregates¶

When using an INT or smaller type, the SUM and COUNT operations return a value of the same type. To avoid overflow on large results, cast the column up to a larger type.

For example

SELECT store_name, SUM(amount :: BIGINT) FROM store_dim
GROUP BY 1;

Prefer `COUNT(*)` and `COUNT` on non-nullable columns¶

BLUE optimizes COUNT(*) queries very strongly. This also applies to COUNT(column_name) on non-nullable columns. Using COUNT(column_name) on a nullable column will operate quickly, but much slower than the previous variations.

Return only required columns¶

Returning only the columns you need to client programs can improve overall query performance. This also reduces the overall result set, which can improve performance in third-party tools.

BLUE is able to optimize out unneeded columns very strongly due to its columnar storage.

Use saved queries to reduce recurring compilation time¶

Saved Queries are compiled when they are created. The query plan is saved in BLUE’s metadata for later re-use.

Because the query plan is saved, they can be used to reduce compilation overhead, especially with very complex queries, such as queries with lots of values in an IN predicate.

When executed, the saved query plan is recalled and executed on the up-to-date data stored on disk.

See how to use saved queries in the saved queries guide.

Pre-filter to reduce JOIN complexity¶

Filter and reduce table sizes prior to joining on them

SELECT store_name,
       SUM(amount)
FROM dimention dim
  JOIN fact ON dim.store_id = fact.store_id
WHERE p_date BETWEEN '2019-07-01' AND '2019-07-31'
GROUP BY store_name;

Can be rewritten as:

SELECT store_name,
       sum_amount
FROM dimention AS dim
  INNER JOIN (SELECT SUM(amount) AS sum_amount,
                     store_id
              FROM fact
              WHERE p_date BETWEEN '2019-07-01' AND '2019-07-31'
              GROUP BY store_id) AS fact ON dim.store_id = fact.store_id;

Data loading considerations¶

Allow and use natural sorting on data¶

Very often, tabular data is already naturally ordered along a dimension such as a timestamp or area.

This natural order is a major factor for query performance later on, as data that is naturally sorted can be more easily compressed and analyzed with BLUE’s metadata collection.

For example, when data is sorted by timestamp, filtering on this timestamp is more effective than filtering on an unordered column.

Natural ordering can also be used for effective DELETE operations.