Testing compression encodings - Amazon Redshift
Example

Testing compression encodings

If you decide to manually specify column encodings, you might want to test different encodings with your data.

Note

We recommend that you use the COPY command to load data whenever possible, and allow the COPY command to choose the optimal encodings based on your data. Or you can use the ANALYZE COMPRESSION command to view the suggested encodings for existing data. For details about applying automatic compression, see Loading tables with automatic compression.

To perform a meaningful test of data compression, you must have a large number of rows. For this example, we create a table and insert rows by using a statement that selects from two tables; VENUE and LISTING. We leave out the WHERE clause that would normally join the two tables. The result is that each row in the VENUE table is joined to all of the rows in the LISTING table, for a total of over 32 million rows. This is known as a Cartesian join and normally is not recommended. However, for this purpose, it's a convenient method of creating many rows. If you have an existing table with data that you want to test, you can skip this step.

After we have a table with sample data, we create a table with seven columns. Each has a different compression encoding: raw, bytedict, lzo, run length, text255, text32k, and zstd. We populate each column with exactly the same data by running an INSERT command that selects the data from the first table.

To test compression encodings, do the following:

  1. (Optional) First, use a Cartesian join to create a table with a large number of rows. Skip this step if you want to test an existing table. 

    create table cartesian_venue(
venueid smallint not null distkey sortkey,
venuename varchar(100),
venuecity varchar(30),
venuestate char(2),
venueseats integer);

insert into cartesian_venue
select venueid, venuename, venuecity, venuestate, venueseats
from venue, listing;

  2. Next, create a table with the encodings that you want to compare. 

    create table encodingvenue (
venueraw varchar(100) encode raw,
venuebytedict varchar(100) encode bytedict,
venuelzo varchar(100) encode lzo,
venuerunlength varchar(100) encode runlength,
venuetext255 varchar(100) encode text255,
venuetext32k varchar(100) encode text32k,
venuezstd varchar(100) encode zstd);

  3. Insert the same data into all of the columns using an INSERT statement with a SELECT clause. 

    insert into encodingvenue
select venuename as venueraw, venuename as venuebytedict, venuename as venuelzo, venuename as venuerunlength, venuename as  venuetext32k, venuename as  venuetext255, venuename as venuezstd
from cartesian_venue;

  4. Verify the number of rows in the new table. 

    select count(*) from encodingvenue

  count
----------
 38884394
(1 row)

  5. Query the STV_BLOCKLIST system table to compare the number of 1 MB disk blocks used by each column.

    The MAX aggregate function returns the highest block number for each column. The STV_BLOCKLIST table includes details for three system-generated columns. This example uses col < 6 in the WHERE clause to exclude the system-generated columns. 

    select col, max(blocknum)
from stv_blocklist b, stv_tbl_perm p
where (b.tbl=p.id) and name ='encodingvenue'
and col < 7
group by name, col
order by col;

    The query returns the following results. The columns are numbered beginning with zero. Depending on how your cluster is configured, your result might have different numbers, but the relative sizes should be similar. You can see that BYTEDICT encoding on the second column produced the best results for this dataset. This approach has a compression ratio of better than 20:1. LZO and ZSTD encoding also produced excellent results. Different datasets produce different results, of course. When a column contains longer text strings, LZO often produces the best compression results.

     col | max
-----+-----
   0 | 203
   1 |  10
   2 |  22
   3 | 204
   4 |  56
   5 |  72
   6 |  20
(7 rows)

If you have data in an existing table, you can use the ANALYZE COMPRESSION command to view the suggested encodings for the table. For example, the following example shows the recommended encoding for a copy of the VENUE table, CARTESIAN_VENUE, that contains 38 million rows. Notice that ANALYZE COMPRESSION recommends LZO encoding for the VENUENAME column. ANALYZE COMPRESSION chooses optimal compression based on multiple factors, which include percent of reduction. In this specific case, BYTEDICT provides better compression, but LZO also produces greater than 90 percent compression. 

analyze compression cartesian_venue;

Table          | Column     | Encoding | Est_reduction_pct
---------------+------------+----------+------------------
reallybigvenue | venueid    | lzo      | 97.54            
reallybigvenue | venuename  | lzo      | 91.71            
reallybigvenue | venuecity  | lzo      | 96.01            
reallybigvenue | venuestate | lzo      | 97.68            
reallybigvenue | venueseats | lzo      | 98.21

Example

The following example creates a CUSTOMER table that has columns with various data types. This CREATE TABLE statement shows one of many possible combinations of compression encodings for these columns. 

create table customer(
custkey int encode delta,
custname varchar(30) encode raw,
gender varchar(7) encode text255,
address varchar(200) encode text255,
city varchar(30) encode text255,
state char(2) encode raw,
zipcode char(5) encode bytedict,
start_date date encode delta32k);

The following table shows the column encodings that were chosen for the CUSTOMER table and gives an explanation for the choices:

Column Data type Encoding Explanation
CUSTKEY int delta CUSTKEY consists of unique, consecutive integer values. Because the differences are one byte, DELTA is a good choice.
CUSTNAME varchar(30) raw CUSTNAME has a large domain with few repeated values. Any compression encoding would probably be ineffective.
GENDER varchar(7) text255 GENDER is very small domain with many repeated values. Text255 works well with VARCHAR columns in which the same words recur.
ADDRESS varchar(200) text255 ADDRESS is a large domain, but contains many repeated words, such as Street, Avenue, North, South, and so on. Text 255 and text 32k are useful for compressing VARCHAR columns in which the same words recur. The column length is short, so text255 is a good choice.
CITY varchar(30) text255 CITY is a large domain, with some repeated values. Certain city names are used much more commonly than others. Text255 is a good choice for the same reasons as ADDRESS.
STATE char(2) raw In the United States, STATE is a precise domain of 50 two-character values. Bytedict encoding would yield some compression, but because the column size is only two characters, compression might not be worth the overhead of uncompressing the data.
ZIPCODE char(5) bytedict ZIPCODE is a known domain of fewer than 50,000 unique values. Certain zip codes occur much more commonly than others. Bytedict encoding is very effective when a column contains a limited number of unique values.
START_DATE date delta32k Delta encodings are very useful for date time columns, especially if the rows are loaded in date order.