I am currently utilizing the UNLOAD feature in AWS Athena, where I query something like:
UNLOAD (SELECT * FROM sometable) TO 's3://<location>' WITH (format = 'TEXTFILE', field_delimeter = ',')
This generates a bunch of .gz files. My issue is that all the null/empty values have been converted to \N. Is there a way to replace this with just an empty string?
I did notice that if I just do SELECT * FROM sometable, basically not using unload, it seems to be what I want (without \N). I would like to get similar results with the unload, if its possible. There seems to be a SERDEPROPERTIES with serialization.null.format (for creating a table), but not sure how to use it with unload.
Related
I have a file structure such as:
gs://BUCKET/Name/YYYY/MM/DD/Filename.csv
Every day my cloud functions are creating another path with another file innit corresponding to the date of the day (so for today's 5th of August) we would have gs://BUCKET/Name/2022/08/05/Filename.csv
I need to find a way to query this data to Big Query automatically so that if I want to query it for 'manual inspection' I can select for example data from all 3 months in one query doing CREATE TABLE with gs://BUCKET/Name/2022/{06,07,08}/*/*.csv
How can I replicate this? I know that BigQuery does not support more than 1 wildcard, but maybe there is a way to do so.
To query data inside GCS from Big Query you can use an external table.
Problem is this will fail because you cannot have a comma (,)
as part of the URI list
CREATE EXTERNAL TABLE `bigquerydevel201912.foobar`
OPTIONS (
format='CSV',
uris = ['gs://bucket/2022/{1,2,3}/data.csv']
)
You have to specify the 3 CSV file locations like this:
CREATE EXTERNAL TABLE `bigquerydevel201912.foobar`
OPTIONS (
format='CSV',
uris = [
'gs://inigo-test1/2022/1/data.csv',
'gs://inigo-test1/2022/2/data.csv']
'gs://inigo-test1/2022/3/data.csv']
)
Since you're using this sporadically, probably makes more sense to create a temporal external table.
se I found a solution that works at least for my use case, without using the external table.
During the creation of table in dataset in BigQuery use create table from: GCS and then when using URI pattern I used gs://BUCKET/Name/2022/* ; As long as filename is the same in each subfolder and schema is identical, then BQ will load everything and then you can perform date operations directly in BQ (I have a column with ingestion date)
I am using aws glue csv crawler to crawl s3 directory containing csv files. Crawler works fine in the sense that it creates the schema with correct data types for each column, however, when I query data from athena, it doesn't show value under boolean type column.
A csv looks like this:
"val","ts","cond"
"1.2841974","15/05/2017 15:31:59","True"
"0.556974","15/05/2017 15:40:59","True"
"1.654111","15/05/2017 15:41:59","True"
And the table created by crawler is:
Column name Data type
val string
ts string
cond boolean
However, when I run say select * from <table_name> limit 10 it returns:
val ts cond
1 "1.2841974" "15/05/2017 15:31:59"
2 "0.556974" "15/05/2017 15:40:59"
3 "1.654111" "15/05/2017 15:41:59"
Does any one has any idea what might be the reason?
I forgot to add, if I change the data type of cond column to string, it does show data as string e.g. "True" or "False"
I don't know why Glue classifies the cond column as boolean, because Athena will not understand that value as a boolean. I think this is a bug in Glue, or an artefact of it not targeting Athena exclusively. Athena expects boolean values to be either true or false. I don't remember if that includes different capitalizations of the strings or not, but either way yours will fail because they are quoted. The actual bug is that Glue has not configured your table so that it strips the quotes from the strings, and therefore Athena sees a boolean column containing "True" with quotes and all, and that is not a supported boolean value. Instead you get NULL values.
You could try changing your tables to use the OpenCSVSerDe instead, it supports quoted values.
It's surprising that Glue continues to stumble on basic things like this. Glue is unfortunately rarely worth the effort over writing some basic scripts yourself.
I'm in the process of building new "ETL" pipelines with CTAS. Unfortunately, Quite often the CTAS query is too intensive which causes Athena to time out. As such, I use CTAS to create the initial table and populate with a small sample. I then write a script that queries the same table the CTAS was generated from (which is parquet format) for the remaining days that the CTAS couldn’t handle upfront. I write the output of these query results to the same directory that is holding the results of the CTAS query before repairing the table (to pick up new data). However, it seems to be a pretty clunky process for a number of reasons:
1) Query results written out with a standard SQL statements all end up being strings. For example, when I write out the number of DAUs (which is a count and cast to an int) the csv output is a string I.e. wrapped in “”.
Is it possible to write out Athena "query_results" (not the CTAS) as anything other than a string when in CSV format. The main problem with this is it means it can't be read back into the table produced by the CTAS since these column expect a bigint. This, of course, can be resolved with a lambda function but seems like a big overhead for something that should be trivial.
2) Can you put query results (not from CTAS) directly into parquet instead of CSV?
3) Is there any way to prevent metadata being generated with the query_results (not from CTAS). Again, it can be cleaned up with a lambda function, but it's just additional nonsense I need to handle.
Thanks in advance!
The data type of the result depends on the SQL used to create it and also on how you consume it. Based on your question I'm going to assume that you're creating a table using CTAS and that the output is CSV, and that you're then looking at the CSV data directly.
That CSV is going to have quotes in it, but that doesn't mean that it's not possible to read integer values as integers, and so on. Athena uses a schema-on-read approach, and as long as the serde can interpret a value as a particular type, that type will work as the type of the column.
If you query the table created by your CTAS operation you should get back integers for the integer columns.
Using CTAS you can also create output of different types, like JSON, Avro, Parquet, and ORC, that keep the type information. Just use the format property to select the output type.
I am a bit confused what you mean by your third question. With a normal query you get two files on S3, the data file and the metadata file, and they will be written to the output location given in the StartQueryExecution API call, but with a CTAS query you get the output data in a different location (given in the SQL) than the metadata file.
Are you actually using CTAS, or are you talking about the regular query result files?
Update after the question got clarified:
1) Athena is unfortunately unable to properly read it's own output in many situations. This is something that really surprises me that they never considered before launch. You might be able to set up a table that uses the regex serde.
2) No, unfortunately the only output of a regular query is CSV at this time.
3) No, the metadata is always written to the same prefix as the output.
I think your best bet is running multiple CTAS queries that select subsets of your source data, if there is a date column for example you could make one CTAS per month or some other time range that works. After the CTAS queries have completed you can move the result files into the same directory on S3 and create a final table that has that directory as its location.
For context: I skimmed this previous question but was dissatisifed with the answer for two reasons:
I'm not writing anything in Python; in fact, I'm not writing any custom scripts for this at all as I'm relying on a crawler and not a Glue script.
The answer is not as complete as I require since it's just a link to some library.
I'm looking to leverage AWS Glue to accept some CSVs into a schema, and using Athena, convert that CSV table into multiple Parquet-formatted tables for ETL purposes. The data I'm working with has quotes embedded in it, which would be okay save for the fact that one record I have has a value of:
"blablabla","1","Freeman,Morgan","bla bla bla"
It seems that Glue is tripping over itself when it encounters the "Freeman,Morgan" piece of data.
If I use the standard Glue crawler, I get a table created with the LazySimpleSerDe, which truncates the record above in its column to:
"Freeman,
...which is obviously not desirable.
How do I force the crawler to output the file with the correct SerDe?
[Unpleasant] Constraints:
Looking to not accomplish this with a Glue script, since for that to work I believe I have to have a table beforehand, whereas the crawler will create the table on my behalf.
If I have to do this all through Amazon Athena, I'd feel like that would largely defeat the purpose but it's a tenable solution.
This is going to turn into a very dull answer, but apparently AWS provides its own set of rules for classifying if a file is a CSV.
To be classified as CSV, the table schema must have at least two
columns and two rows of data. The CSV classifier uses a number of
heuristics to determine whether a header is present in a given file.
If the classifier can't determine a header from the first row of data,
column headers are displayed as col1, col2, col3, and so on. The
built-in CSV classifier determines whether to infer a header by
evaluating the following characteristics of the file:
Every column in a potential header parses as a STRING data type.
Except for the last column, every column in a potential header has content that is fewer than 150 characters. To allow for a trailing
delimiter, the last column can be empty throughout the file.
Every column in a potential header must meet the AWS Glue regex requirements for a column name.
The header row must be sufficiently different from the data rows. To determine this, one or more of the rows must parse as other than
STRING type. If all columns are of type STRING, then the first row of
data is not sufficiently different from subsequent rows to be used as
the header.
I believed that I had met all of these requirements, given that the column names are wildly divergent from the actual data in the CSV, and ideally there shouldn't be much of an issue there.
However, in spite of my belief that it would satisfy the AWS Glue regex (which I can't find a definition for anywhere), I elected to move away from commas and to pipes instead. The data now loads as I expect it to.
Use glueContext.create_dynamic_frame_from_options() while converting csv to parquet and then run crawler over parquet data.
df = glueContext.create_dynamic_frame_from_options("s3", {"paths": [src]}, format="csv")
Default separator is ,
Default quoteChar is "
If you wish to change then check https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-format.html
I am able to unload data to S3, and query the results with Spectrum, but NOT when using the delimiter defined below. This is our standard delimiter that works with all of our processing today related to Redshift COPY and UNLOAD commands, so I believe the UNLOAD is working fine. But somewhere between the table definition and the SQL query to retrieve the data, this is not working. We just receive NULLS for all of the fields. Can you look at our example below in order to determine next steps.
unload ('select * from db.test')
to 's3://awsbucketname/ap_cards/'
iam_role 'arn:aws:iam::123456789101:role/redshiftaccess'
delimiter '\325'
manifest;
CREATE EXTERNAL TABLE db_spectrum.test (
cost_center varchar(100) ,
fleet_service_flag varchar(1)
)
row format delimited
fields terminated by '\325'
stored as textfile
location 's3://awsbucketname/test/';
select * from db_spectrum.test
Got a response from AWS Support center as:
Unfortunately you will need to either process the data externally to change the delimiter or UNLOAD the data again with a different delimiter.
The docs say to Specify a single ASCII character for 'delimiter'.
The ASCII range only goes up to 177 in octal.
We will clarify the docs to note that 177 is the max permissible octal for a delimiter. I can confirm that this is the same in Athena as well.
Thank you for bringing this to our attention.
You might try using Spectrify for this. It automates a lot of the nastiness involved currently with moving redshift table to spectrum.