Redshift check stl_load_errors system table for details

STL_LOAD_ERRORS

Displays the records of all Amazon Redshift load errors.

STL_LOAD_ERRORS contains a history of all Amazon Redshift load errors. See Load error reference for a comprehensive list of possible load errors and explanations.

Query STL_LOADERROR_DETAIL for additional details, such as the exact data row and column where a parse error occurred, after you query STL_LOAD_ERRORS to find out general information about the error.

This view is visible to all users. Superusers can see all rows; regular users can see only their own data. For more information, see Visibility of data in system tables and views.

Table columns

[See the AWS documentation website for more details]

Sample queries

The following query joins STL_LOAD_ERRORS to STL_LOADERROR_DETAIL to view the details errors that occurred during the most recent load.

select d.query, substring(d.filename,14,20), d.line_number as line, substring(d.value,1,16) as value, substring(le.err_reason,1,48) as err_reason from stl_loaderror_detail d, stl_load_errors le where d.query = le.query and d.query = pg_last_copy_id(); query | substring | line | value | err_reason -------+-------------------+------+----------+---------------------------- 558| allusers_pipe.txt | 251 | 251 | String contains invalid or unsupported UTF8 code 558| allusers_pipe.txt | 251 | ZRU29FGR | String contains invalid or unsupported UTF8 code 558| allusers_pipe.txt | 251 | Kaitlin | String contains invalid or unsupported UTF8 code 558| allusers_pipe.txt | 251 | Walter | String contains invalid or unsupported UTF8 code

The following example uses STL_LOAD_ERRORS with STV_TBL_PERM to create a new view, and then uses that view to determine what errors occurred while loading data into the EVENT table:

create view loadview as (select distinct tbl, trim(name) as table_name, query, starttime, trim(filename) as input, line_number, colname, err_code, trim(err_reason) as reason from stl_load_errors sl, stv_tbl_perm sp where sl.tbl = sp.id);

Next, the following query actually returns the last error that occurred while loading the EVENT table:

select table_name, query, line_number, colname, starttime, trim(reason) as error from loadview where table_name ='event' order by line_number limit 1;

The query returns the last load error that occurred for the EVENT table. If no load errors occurred, the query returns zero rows. In this example, the query returns a single error:

table_name | query | line_number | colname | error | starttime ------+-----+----+----+--------------------------------------------------------+---------------------- event | 309 | 0 | 5 | Error in Timestamp value or format [%Y-%m-%d %H:%M:%S] | 2014-04-22 15:12:44 (1 row)

In cases where the COPY command automatically splits large, uncompressed, text-delimited file data to facilitate parallelism, the line_number, is_partial, and start_offset columns show information pertaining to splits. (The line number can be unknown in cases where the line number from the original file is unavailable.)

--scan ranges information SELECT line_number, POSITION, btrim(raw_line), btrim(raw_field_value), btrim(err_reason), is_partial, start_offset FROM stl_load_errors WHERE query = pg_last_copy_id(); --result -1,51,"1008771|13463413|463414|2|28.00|38520.72|0.06|0.07|NO|1998-08-30|1998-09-25|1998-09-04|TAKE BACK RETURN|RAIL|ans cajole sly","NO","Char length exceeds DDL length",1,67108864

The COPY command helps you to load data into a table from data files or from an Amazon DynamoDB table. The files can be located in an Amazon Simple Storage Service (Amazon S3) bucket, an Amazon EMR cluster, or a remote host that is accessed using a Secure Shell (SSH) connection.

You can add data to your Amazon Redshift tables either by using an INSERT command or by using a COPY command. At the scale and speed of an Amazon Redshift data warehouse, the COPY command is many times faster and more efficient than INSERT commands.

The COPY command uses the Amazon Redshift massively parallel processing (MPP) architecture to read and load data in parallel from multiple data sources. You can load from data files on Amazon S3, Amazon EMR, or any remote host accessible through a Secure Shell (SSH) connection, or you can load directly from an Amazon DynamoDB table.

Reference: //docs.aws.amazon.com/redshift/latest/dg/tutorial-loading-data.html

What is STL_LOAD_ERRORS system table?

STL_LOAD_ERRORS table displays the records of all Amazon Redshift load errors.

Let’s say you executed a COPY command to load data to an Amazon Redshift database and got an error that tells you :

Check ‘stl_load_errors’ system table for details

There is no error description and whenever you get this error, you should run a SELECT query on STL_LOAD_ERRORS table and this table has 16 columns.

SELECT * FROM stl_load_errors;

You should look at: line_number, colname, raw_line, raw_field_value and err_reason columns to identify the error. This takes time, a lot of time.

The right & fast way: Instant Load Errors

DataRow has a unique feature System Tables Panel which shows LOAD errors information about how the Amazon Redshift system is functioning. You can view these tables without querying your database.

Instant Load Errors feature runs just after you execute your query and gives you the error result below your query editor. Therefore, you get immediate information to troubleshoot data load issues. Additionally, you can see the records of all system tables in one place without performing any additional query. Also, you can understand how your Amazon Redshift system behaves.

As you can see that; DataRow Query Editor returns all the details about the error after you executed a COPY command which normally returns “Check ‘stl_load_errors’ system table for details.”

COPY Command Error History

To access the error history of executed COPY commands, normally you should run a SELECT query to the same table ( STL_LOAD_ERRORS ). It’s a basic SELECT query but it’s hard to analyze this table since it has 16 columns, and contains lots of information. Identifying an error takes too much time.

System Tables in DataRow contain information about how the system is functioning. You can view these tables without querying your database.

DataRow System Tables for Amazon Redshift makes your life easier. You can access the records of all Amazon Redshift load errors (and also other Amazon Redshift System tables such as: STL_FILE_SCAN and STL_S3_CLIENT_ERROR).

DataRow provides you a great UI to analyze the Amazon Redshift System Tables.

How do I get Redshift table information?

How to Show, List or Describe Tables in Amazon Redshift.
SELECT * FROM PG_TABLE_DEF; ... .
SELECT * FROM PG_TABLE_DEF WHERE schemaname = 'public'; ... .
SELECT DISTINCT tablename FROM PG_TABLE_DEF WHERE schemaname = 'public'; ... .
tablename --------- category date event listing sales users venue..

How do I check Redshift errors?

If you can find the pid , you can query stl_query table to find out if are looking at right query. Then, check the stl_error table for your pid . This will tell you the error you are looking for.

How do you check all tables in Redshift?

To view a list of all tables in a schema, you can query the PG_TABLE_DEF system catalog table. You can first examine the setting for search_path . The following example adds the SALES schema to the search path and shows all the tables in the SALES schema.

How do you check schema privileges in Redshift?

To view the permissions of a specific user on a specific schema, simply change the bold user name and schema name to the user and schema of interest on the following code. For a full list of every user - schema permission status, simply delete the entire WHERE clause. SELECT u. usename, s.

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