Is there an option to load a CSV into Redshift while skipping over a footer?
Just like we use ignoreheader when we want to ignore initial rows. If we want to ignore last rows is there any way?
No. There is no parameter to tell the COPY command to ignore rows at the end of a file.
However, you could load the file with an error by specifying a MAXERROR of 1, which will allow the file to load with one bad row (or more, if required).
Related
Say I have a BigQuery table that contains 3M rows, and I want to export it to gcs.
What I do is standard bq extract <flags> ... <project_id>:<dataset_id>.<table_id> gs://<bucket>/file_name_*.<extension>
I am bound by a limit on the number of rows a file (part) can have. Is there a way to set a hard limit to the size of a file part?
For example, If I want each partition not to be above 10Mb for example, or even better, to set the maximum number of rows allowed to go in a file part? The documentation doesn't seem to mention any flags for this purpose.
You can't do it with BigQuery extract API.
But you can script it (perform an export of thousands of row in a loop) but you will have to pay for the processed data (the extract is free!). You can also set up a Dataflow job for this (but it's also not free!).
Looking for a way to process ~ 4Gb file which is a result of Athena query and I am trying to know:
Is there some way to split Athena's query result file into small pieces? As I understand - it is not possible from Athena side. Also, looks like it is not possible to split it with Lambda - this file too large and looks like s3.open(input_file, 'r') does not work in Lambda :(
Is there some other AWS services that can solve this issue? I want to split this CSV file to small (about 3 - 4 Mb) to send them to external source (POST requests)
You can use the option to CTAS with Athena and use the built-in partition capabilities.
A common way to use Athena is to ETL raw data into a more optimized and enriched format. You can turn every SELECT query that you run into a CREATE TABLE ... AS SELECT (CTAS) statement that will transform the original data into a new set of files in S3 based on your desired transformation logic and output format.
It is usually advised to have the newly created table in a compressed format such as Parquet, however, you can also define it to be CSV ('TEXTFILE').
Lastly, it is advised to partition a large table into meaningful partitions to reduce the cost to query the data, especially in Athena that is charged by data scanned. The meaningful partitioning is based on your use case and the way that you want to split your data. The most common way is using time partitions, such as yearly, monthly, weekly, or daily. Use the logic that you would like to split your files as the partition key of the newly created table.
CREATE TABLE random_table_name
WITH (
format = 'TEXTFILE',
external_location = 's3://bucket/folder/',
partitioned_by = ARRAY['year','month'])
AS SELECT ...
When you go to s3://bucket/folder/ you will have a long list of folders and files based on the selected partition.
Note that you might have different sizes of files based on the amount of data in each partition. If this is a problem or you don't have any meaningful partition logic, you can add a random column to the data and partition with it:
substr(to_base64(sha256(some_column_in_your_data)), 1, 1) as partition_char
Or you can use bucketing and provide how many buckets you want:
WITH (
format = 'TEXTFILE',
external_location = 's3://bucket/folder/',
bucketed_by = ARRAY['column_with_high_cardinality'],
bucket_count = 100
)
You won't be able to do this with Lambda as your memory is maxed out around 3GB and your file system storage is maxed out at 512 MB.
Have you tried just running the split command on the filesystem (if you are using a Unix based OS)?
If this job is reoccurring and needs to be automated and you wanted to still be "serverless", you could create a Docker image that contains a script to perform this task and then run it via a Fargate task.
As for the specific of how to use split, this other stack overflow question may help:
How to split CSV files as per number of rows specified?
You can ask S3 for a range of the file with the Range option. This is a byte range (inclusive), for example bytes=0-1000 to get the first 1000 bytes.
If you want to process the whole file in the same Lambda invocation you can request a range that is about what you think you can fit in memory, process it, and then request the next. Request the next chunk when you see the last line break, and prepend the partial line to the next chunk. As long as you make sure that the previous chunk gets garbage collected and you don't aggregate a huge data structure you should be fine.
You can also run multiple invocations in parallel, each processing its own chunk. You could have one invocation check the file size and then invoke the processing function as many times as necessary to ensure each gets a chunk it can handle.
Just splitting the file into equal parts won't work, though, you have no way of knowing where lines end, so a chunk may split a line in half. If you know the maximum byte size of a line you can pad each chunk with that amount (both at the beginning and end). When you read a chunk you skip ahead until you see the last line break in the start padding, and you skip everything after the first line break inside the end padding – with special handling of the first and last chunk, obviously.
My actual data in csv extracts starts from line 10. How can I skip top few lines in snowflake load using copy or any other utility. Do we have anything similar to SKIP_HEADER ?
I have files on S3 and its my stage. I would be creating a snowpipe later on this datasource.
yes there is a skip_header option for CSV, allowing you to skip a specified number of rows, when defining a file format. Please have a look here:
https://docs.snowflake.net/manuals/sql-reference/sql/create-file-format.html#type-csv
So you create a file format associated with the csv files you have in mind and then use this when calling the copy commands.
i have many .csv files which are stored into gcs and i want to load data from.csv to BigQuery using below commands:
bq load 'datasate.table' gs://path.csv json_schema
i have tried but giving errors, same error is giving for many file.
error screenshot
how can i remove unwanted values from .csv files before importing into table.
Suggest me to load file in easiest way
The answer depends on what do you want to do with this junk rows. If you look at the documentation, you have several options
Number of errors allowed. By default, it's set to 0 and that why the load job fails at the first line. If you know the total number of rom, set this value to the Number of errors allowed and all the errors will be ignored in the Load Job
Ignore unknown values. If your errors are made because some line contains more column as defined in the schema, this option keep the line in error and only the known column, the others are ignore
Allow jagged rows. If your errors are made by too short line (and it is in your message) and you still want to keep the first columns (because the last ones are optional and/or not relevant), you can check this option
For more advanced and specific filters, you have to perform pre or post processing. If it's the case, let me know to add this part to my answer.
Is there any way/option or workaround to skip the entire file which contains bad entries , while loading the data from S3 to Redshift.
Please note that I am not talking about skipping the entries that are invalid in the file, but the entire file which contains bad entry or record.
By default Redshift fails entire file if you don't supply Maxerror option in Copy command. Its default behavior.
copy catdemo from 's3://awssampledbuswest2/tickit/category_pipe.txt' iam_role 'arn:aws:iam::<aws-account-id>:role/<role-name>' region 'us-west-2';
Above command will fail entire file and will not load any data from given file. Read the documentation here for more information.
If you specify, Maxerror option then only it ignores records upto that # from particular file.
copy catdemo from 's3://awssampledbuswest2/tickit/category_pipe.txt' iam_role 'arn:aws:iam::<aws-account-id>:role/<role-name>' region 'us-west-2' MAXERROR 500;
In above example Redshift will tolerate up-to 500 bad records.
I hope this answers your question, but If it doesn't please update the question and I will refocus the answer.