Copy data from Amazon S3 to Redshift and avoid duplicate rows

Question

I am copying data from Amazon S3 to Redshift. During this process, I need to avoid the same files being loaded again. I don't have any unique constraints on my Redshift table. Is there a way to implement this using the copy command?

http://docs.aws.amazon.com/redshift/latest/dg/r_COPY_command_examples.html

I tried adding unique constraint and setting column as primary key with no luck. Redshift does not seem to support unique/primary key constraints.

Solution 1

My solution is to run a 'delete' command before 'copy' on the table. In my use case, each time I need to copy the records of a daily snapshot to redshift table, thus I can use the following 'delete' command to ensure duplicated records are deleted, then run the 'copy' command.

DELETE from t_data where snapshot_day = 'xxxx-xx-xx';

Solution 2

As user1045047 mentioned, Amazon Redshift doesn't support unique constraints, so I had been looking for the way to delete duplicate records from a table with a delete statement. Finally, I found out a reasonable way.

Amazon Redshift supports creating an IDENTITY column that is stored an auto-generated unique number. http://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_TABLE_NEW.html

The following sql is for PostgreSQL to delete duplicated records with OID that is unique column, and you can use this sql by replacing OID with the identity column.

DELETE FROM duplicated_table WHERE OID > (
　SELECT MIN(OID) FROM duplicated_table d2
　　WHERE column1 = d2.dupl_column1
　　AND column2 = d2.column2
);

Here is an example that I tested on my Amazon Redshift cluster.

create table auto_id_table (auto_id int IDENTITY, name varchar, age int);

insert into auto_id_table (name, age) values('John', 18);
insert into auto_id_table (name, age) values('John', 18);
insert into auto_id_table (name, age) values('John', 18);
insert into auto_id_table (name, age) values('John', 18);
insert into auto_id_table (name, age) values('John', 18);
insert into auto_id_table (name, age) values('Bob', 20);
insert into auto_id_table (name, age) values('Bob', 20);  
insert into auto_id_table (name, age) values('Matt', 24); 

select * from auto_id_table order by auto_id; 
 auto_id | name | age 
---------+------+-----
       1 | John |  18
       2 | John |  18
       3 | John |  18
       4 | John |  18
       5 | John |  18
       6 | Bob  |  20
       7 | Bob  |  20
       8 | Matt |  24    
(8 rows) 

delete from auto_id_table where auto_id > (
  select min(auto_id) from auto_id_table d
    where auto_id_table.name = d.name
    and auto_id_table.age = d.age
);

select * from auto_id_table order by auto_id;
 auto_id | name | age 
---------+------+-----
       1 | John |  18
       6 | Bob  |  20
       8 | Matt |  24
(3 rows)

Also it works with COPY command like this.

• auto_id_table.csv

  John,18
  Bob,20
  Matt,24
  

• copy sql

  copy auto_id_table (name, age) from '[s3-path]/auto_id_table.csv' CREDENTIALS 'aws_access_key_id=[your-aws-key-id] ;aws_secret_access_key=[your-aws-secret-key]' delimiter ','; 
  

The advantage of this way is that you don't need to run DDL statements. However it doesn't work with existing tables that do not have an identity column because an identity column cannot be added to an existing table. The only way to delete duplicated records with existing tables is migrating all records like this. (same as user1045047's answer)

insert into temp_table (select distinct from original_table);
drop table original_table;
alter table temp_table rename to original_table;

Solution 3

Mmm..

What about just never loading data into your master table directly.

Steps to avoid duplication:

1. begin transaction
2. bulk load into a temp staging table
3. delete from master table where rows = staging table rows
4. insert into master table from staging table (merge)
5. drop staging table
6. end transaction.

this is also super somewhat fast, and recommended by redshift docs.

Solution 4

Currently there is no way to remove duplicates from redshift. Redshift doesn't support primary key/unique key constraints, and also removing duplicates using row number is not an option (deleting rows with row number greater than 1) as the delete operation on redshift doesn't allow complex statements (Also the concept of row number is not present in redshift).

The best way to remove duplicates is to write a cron/quartz job that would select all the distinct rows, put them in a separate table and then rename the table to your original table.

Insert into temp_originalTable (Select Distinct from originalTable)

Drop table originalTable

Alter table temp_originalTable rename to originalTable

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Comments

[Rups N]

I have used a similar solution.

[Rups N]

We cannot implement this while we add new records to the table. We checked the table first before inserting it.In case records exist we would delete the row prior to inserting.

[Kevin Meredith]

It's not clear to me how your copy sql indicates that it handles duplicates properly. For the data set that you're loading, audo_id_table.csv, it simply has 3 unique rows, no?

[Masashi M]

The point is that you can delete duplicated records easily. Even if you run copy auto_id_table three times and get three duplicated records, those duplicated records will be deleted with the above delete from auto_id_table.... query.

[Tommy]

Can you please elaborate on the "this is also super fast" part of this answer? We have a table that is many billions of rows, and we use this method as recommended by the Amazon docs, but unless we are doing something wrong, it is certainly not super fast. This is by far the bottleneck of our ETL process.

[Kyle Gobel]

I guess super fast is quite subjective. I have only worked in the millions of rows, not many billions...but there isn't a faster process than this to ensure no duplicate records that I have heard of yet. (Interested to hear though if you have something)

[Kevin Meredith]

With this approach, do you need to run the VACUUM command? I read here: If you use multiple concurrent COPY commands to load one table from multiple files, Amazon Redshift is forced to perform a serialized load, which is much slower and requires a VACUUM at the end if the table has a sort column defined

[Masashi M]

Running VACUUM and ANALYZE is better after this, but not necessary because VACUUM takes some costs. In my case, since the number of duplicate records are small, I schedule running VACUUM once a day, so I don't run it with this query.

[webbo]

Have you considered using a transaction?

[nitzien]

Problem with this is when staging table has duplicate records.

[Kyle Gobel]

Yeah, if staging table gets duplicate rows you'll insert duplicates. What we do is write our merge sql with that in mind...more or less group by the composite key and do any necessary aggregates or first_value to avoid duplicates when inserting.