Optimization and best practices

This topic explains some best practices of working with SQream DB.

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 VARCHAR will bring performance and storage footprint improvements, and in many cases huge performance improvements (as well as data integrity benefits). SQream DB stores dates and datetimes very efficiently and can strongly optimize queries using these specific types.

Reduce varchar length to a minimum

With the VARCHAR type, the length has a direct effect on query performance.

If the size of your column is predictable, by defining an appropriate column length (no longer than the maximum actual value) you will get the following benefits:

  • Data loading issues can be identified more quickly
  • SQream DB can reserve less memory for decompression operations
  • Third-party tools that expect a data size are less likely to over-allocate memory

Don’t flatten or denormalize data

SQream DB 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.

Convert foreign tables to native tables

SQream DB’s native storage is heavily optimized for analytic workloads. It is always faster for querying than other formats, even columnar ones such as Parquet. It also enables the use of additional metadata to help speed up queries, in some cases by many orders of magnitude.

You can improve the performance of all operations by converting foreign tables into native tables by using the CREATE TABLE AS syntax.

For example,

CREATE TABLE native_table AS SELECT * FROM external_table

The one situation when this wouldn’t be as useful is when data will be only queried once.

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.

Keep VARCHAR lengths to a minimum

While it won’t make a big difference in storage, large strings allocate a lot of memory at query time.

If a column’s string length never exceeds 50 characters, specify VARCHAR(50) rather than an arbitrarily large number.

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 SQream DB reads during query execution.
  • Where possible, sort columns with the lowest cardinality first. Avoid sorting VARCHAR and TEXT/NVARCHAR 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;

Use the high selectivity hint

Use the high selectivity hint when you expect a predicate to filter out most values.

For example,

SELECT store_name, SUM(amount) FROM store_dim
WHERE HIGH_SELECTIVITY(p_date = '2018-07-01')
GROUP BY 1;

This hint tells the query compiler that the WHERE condition is expected to filter out more than 50% of values. It does not affect the query results, but when used correctly can improve query performance.

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

SQream DB 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.

SQream 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 SQream DB’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 SQream DB’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.