What I understand from the AWS Glue docs is a craweler will help crawl and discover new data. However, I noticed that once I crawled once, if new data goes into S3, the data is actually already discovered when I query the data catalog from Athena for example. So, can I say I do not need a crawler to crawl everytime new data is added, unless there are new schemas?
In fact, if I know the schema of the files, I can just manually create the table and do without a crawler, am I correct?
If data is partitioned by some keys (placed in sub-folders, like /data/year=2018/month=11/day=2) then you need a crawler to register newly added partitions (ie. /day=3) in Data Catalog to be able to query it via Athena.
However, if data is not partitined or comes into already registered partitions then there is no need to run a crawler.
Alternatively to runnig a crawler you can discover and register new partitions by running Athena command MSCK REPAIR TABLE <table> or registering them manually.
The easiest way to create a table in Data Catalog is running a crawler. But if you know schema and have patience to compose CREATE TABLE Athena query or fill all fields via AWS Glue console then you can go that way as well.
If you have the schema then you don't need to use the crawler and you might get better results (the crawler assumes partition columns are strings for example).
As Yuriy says, remember to run MSCK REPAIR TABLE or register new partitions manually.
MSCK can time out if you've added a lot of partitions. If it does, keep running it until it completes normally.
Related
I am adding a new file in parquet format which is created by a Glue Databrew in my S3 folder. The new file has the same schema as the previous file. But when I am running the Crawler for the 2nd time it is neither updating the table nor creating a new one in the data catalog. Also when I am crawling both the files together, both of them are getting added.
Log File is giving the following information:
INFO : Created partitions with values [[New file name]] for table
BENCHMARK : Finished writing to Catalog
I have tried with and without "Create a single schema for each S3 path". But the crawler is not updating the table with the new file. Sooner I will add new files on a daily basis to do my analysis. Any solution?
The best way to approach this issue in my opinion is to use AWS DataBrew output to Data Catalog directly. Data Catalog can be updated either by the crawler or by DataBrew directly but the recommended practice is that you employ any one of those mechanisms not both.
Can you try running the job with output as your data catalog and let Databrew manage your catalog? It should update your catalog table with right data/files.
I want too load data from S3 to Redshift. The data coming to S3 in around 5MB{approximate size} per sec.
I need to automate the loading of data from S3 to Redshift.
The data to S3 is dumping from the kafka-stream consumer application.
The folder S3 data is in folder structure.
Example folder :
bucketName/abc-event/2020/9/15/10
files in this folder :
abc-event-2020-9-15-10-00-01-abxwdhf. 5MB
abc-event-2020-9-15-10-00-02-aasdljc. 5MB
abc-event-2020-9-15-10-00-03-thntsfv. 5MB
the files in S3 have json objects separated with next line.
This data need to be loaded to abc-event table in redshift.
I know few options like AWS Data pipeline, AWS Glue, AWS Lambda Redshift loader (https://aws.amazon.com/blogs/big-data/a-zero-administration-amazon-redshift-database-loader/).
What would be the best way to do it.
Really appreciate if someone will guide me.
Thanks you
=============================================
Thanks Prabhakar for the answer. Need some help in continuation on this.
Created a table in Data Catalog by crawler and
then running a ETLL job in glue does the job of loading the data from S3 to redshift.
I am using approach 1. Predicate pushdown
New files get loaded in S3 in different partition say (new hour started.)
I am adding new partition using a AWS Glue python script job.
Adding new partition in the table using Athena API. (using ALTER TABLE ADD PARTITION).
I have checked in the console that the new partition gets added by the python script job. I checked new partion gets added in Data catalog table.
When I run the same job with pushdown predicate giving same partition added by the python script glue job.
The job did not load the new files from S3 in this new partition to Redshift.
I cant figure out what I am doing wrong ???
In your use case you can leverage AWS Glue to load the data periodically into redshift.You can schedule your Glue job using trigger to run every 60 minutes which will calculate to be around 1.8 GB in your case.
This interval can be changed according to your needs and depending on how much data that you want to process each run.
There are couple of approaches you can follow in reading this data :
Predicate pushdown :
This will only load the partitions that mentioned in the job. You can calculate the partition values every run on the fly and pass them to the filter. For this you need to run Glue crawler each run so that the table partitions are updated in the table metadata.
If you don't want to use crawler then you can either use boto3 create_partition or Athena add partition which will be a free operation.
Job bookmark :
This will load only the latest s3 data that is accumulated from the time that your Glue job completed it's previous run.This approach might not be effective if there is no data generated in S3 in some runs.
Once you calculate the data that is to be read you can simply write it to redshift table every run.
In your case you have files present in sub directories for which you need to enable recurse as shown in below statement.
datasource0 = glueContext.create_dynamic_frame.from_catalog(database =<name>, table_name = <name>, push_down_predicate = "(year=='<2019>' and month=='<06>')", transformation_ctx = "datasource0", additional_options = {"recurse": True})
In AWS Glue, Although I read documentation, but I didn't get cleared one thing. Below is what I understood.
Regarding Crawlers: This will create a metadata table for either S3 or DynamoDB table. But what I don't understand is: how does Scala/Python script able to retrieve data from Actual Source (say DynamoDB or S3) using Metadata created tables.
val input = glueContext
.getCatalogSource(database = "my_data_base", tableName = "my_table")
.getDynamicFrame()
Does above line retrieve data from actual source via metadata tables?
I will be glad if someone can able to explain me behind the scenes of retrieving data in Glue script via metadata tables.
When you run a Glue crawler it will fetch metadata from S3 or JDBC (depends on your requirement) and creates tables in AWS Glue Data Catalog.
Now if you want to connect to this data/tables from Glue ETL job then you can do it in multiple ways depending on your requirement:
[from_options][1] : if you want to load directly from S3/JDBC with out connecting to Glue catalog.
[from_catalog][1] : If you want to load data from Glue catalog then you need to link it with catalog using getCatalogSource method as shown in your code. As the name infers it will use Glue data catalog as source and load particular table that you pass to this method.
Once it looks at your table definition which is pointed to a location then it will make a connection and load the data present in the source.
Yes you need to use getCatalogSource if you want to load tables from Glue catalog.
Does Catalog look into Crawler and refer to actual source and load data?
Check out the diagram in this [link][2] . It will give you an idea about the flow.
What if crawler deleted before I run getCatalogSource, then will I can able to load data in this case?
Crawler and Table are two different components. It all depends on when the table is deleted. If you delete the table after your job start to execute then there will not be any problem. If you delete it before execution starts then you will encounter an error.
What if my Source has lots of million of records? then will this load all records or how in this case?
It is good to have large files to be present in source so it will avoid most of the small files problem. Glue based on Spark and it will read files which can be fit in memory and then do the computations. Check this [answer][3] and [this][4] for best practices while reading larger files in AWS Glue.
[1]: https://docs.aws.amazon.com/glue/latest/dg/aws-glue-api-crawler-pyspark-extensions-dynamic-frame-reader.html
[2]: https://docs.aws.amazon.com/athena/latest/ug/glue-athena.html
[3]: https://stackoverflow.com/questions/46638901/how-spark-read-a-large-file-petabyte-when-file-can-not-be-fit-in-sparks-main
[4]: https://aws.amazon.com/blogs/big-data/optimize-memory-management-in-aws-glue/#:~:text=Incremental%20processing:%20Processing%20large%20datasets
I'm working with AWS glue and many files on s3, with new files appended every day. I try to create and run a crawler to deduce a schema of those csv files. Instead of just one data catalog table with schema, crawler creates many tables (even with Create a single schema for each S3 path option selected), which means that crawler recognize different schemas and can't combine them into one. But I need just one table in data catalog for all those files!
So I created separate data catalog table manually, and when I use this table with glue job, none of the s3 csv files are processed. I guess that is because every time crawler runs, it checks for new files and partitions (and in good case of single schema table we can see those files and partitions by clicking on View partitions button in Tables).
So in here there is way to update manually created table with a crawler, I followed it with a hope that crawler will not change data types for columns that I selected, but update list of files and partitions for glue job to process later:
You might want to create AWS Glue Data Catalog tables manually and then keep them updated with AWS Glue crawlers. Crawlers running on a schedule can add new partitions and update the tables with any schema changes. This also applies to tables migrated from an Apache Hive metastore.
To do this, when you define a crawler, instead of specifying one or more data stores as the source of a crawl, you specify one or more existing Data Catalog tables. The crawler then crawls the data stores specified by the catalog tables. In this case, no new tables are created; instead, your manually created tables are updated.
It doesn't happen for some reason, in crawler log I see this:
INFO : Some files do not match the schema detected. Remove or exclude the following files from the crawler (truncated to first 200 files):
bucket1/customer/dt=2020-02-26/delta_20200226_080101.csv
INFO : Multiple tables are found under location bucket1/customer/. Table customer is skipped.
But there is no "Exclude patterns" option to exclude that file when crawler uses existing data catalog table, documentation says that in this case "The crawler then crawls the data stores specified by the catalog tables".
And crawler doesn't add any partitions or files to my table.
Is there a way to update my manually created table with new files from s3?
Considering your crawler is detecting different schemas, it will continue to do the same no matter what option I choose. You can get it to use the table definition from the table for all the partitions and then only log changes to avoid updating the table schema. But if there is a difference in schema for the files , I’m not sure if your queries will work.
Another option would be to add partitions using boto3 for your s3 path. I can get the table schema using the get table function and then create a partition in glue with that table schema
I don't know why, but the crawler I created can't update list of files and partitions for glue job to process later, it skips my manually created data catalog table, I see it in the cloudwatch log. To solve this problem, I needed to add repair table query into my glue script, so it does what crawler is supposed to do (and I disabled the crawler itself, so it doesn't changes my manually created table and doesn't create many tables for individual csv files and partitions), before actual ETL process:
import boto3
...
# Athena query part
client = boto3.client('athena', region_name='us-east-2')
data_catalog_table = "customer"
db = "inner_customer" # glue data_catalog db, not Postgres DB
# this supposed to update all partitions for data_catalog_table, so glue job can upload new file data into DB
q = "MSCK REPAIR TABLE "+data_catalog_table
# output of the query goes to s3 file normally
output = "s3://bucket_to_store_query_results/results/"
response = client.start_query_execution(
QueryString=q,
QueryExecutionContext={
'Database': db
},
ResultConfiguration={
'OutputLocation': output,
}
)`
After that query "MSCK REPAIR TABLE customer" executes, it writes to s3://bucket_to_store_query_results/results/ a xxx-xxx-xxx.txt file with content like this:
Partitions not in metastore: customer:dt=2020-03-28 customer:dt=2020-03-29 customer:dt=2020-03-30
Repair: Added partition to metastore customer:dt=2020-03-28
Repair: Added partition to metastore customer:dt=2020-03-29
Repair: Added partition to metastore customer:dt=2020-03-30
And if I open Glue->Tables-> select customer table, then click on "View partitions" button on the right top of the page, I see all my partitions from the s3 bucket. After that part the glue job continues as before. I understand that "repair table" query hack is not really optimal, and may be will change it to something more sophisticated, like described in here.
We have a very large number of folders and files in S3, all under one particular folder, and we want to crawl for all the CSV files, and then query them from one table in Athena. The CSV files all have the same schema. The problem is that the crawler is generating a table for every file, instead of one table. Crawler configurations have a checkbox option to "Create a single schema for each S3 path" but this doesn't seem to do anything.
Is what I need possible? Thanks.
Glue crawlers claims to solve many problems, but in fact solves few. If you're slightly outside the scope of what they designed for you're out of luck. There might be a way to configure it to do what you want, but in my experience trying to make Glue crawlers do things that aren't perfectly aligned with it is not worth the effort.
It sounds like you have a good idea of what the schema of your data is. When that is the case Glue crawlers also provide very little value. You probably have a better idea of what the schema should look than Glue will ever be able to figure out.
I suggest that you manually create the table, and write a one off script that lists all the partition locations on S3 that you want to include in the table and generate ALTER TABLE ADD PARTITION … SQL, or Glue API calls to add those partitions to the table.
To keep the table up to date when new partition locations are added, have a look at this answer for guidance: https://stackoverflow.com/a/56439429/1109
One way to do what you want is to use just one of the tables created by the crawler as an example, and create a similar table manually (in AWS Glue->Tables->Add tables, or in Athena itself, with
CREATE EXTERNAL TABLE `tablename`(
`column1` string,
`column2` string, ...
using existing table as an example, you can see the query used to create that table in Athena when you go to Database -> select your data base from Glue Data Catalog, then click on 3 dots in front of the one "automatically created by crawler table" that you choose as an example, and click on "Generate Create table DDL" option. It will generate a big query for you, modify it as necessary (I believe you need to look at LOCATION and TBLPROPERTIES parts, mostly).
When you run this modified query in Athena, a new table will appear in Glue data catalog. But it will not have any information about your s3 files and partitions, and crawler most likely will not update metastore info for you. So you can in Athena run "MSCK REPAIR TABLE tablename;" query (it's not very efficient, but works for me), and it will add missing file information, in the Result tab you will see something like (in case you use partitions on s3, of course):
Partitions not in metastore: tablename:dt=2020-02-03 tablename:dt=2020-02-04
Repair: Added partition to metastore tablename:dt=2020-02-03
Repair: Added partition to metastore tablename:dt=2020-02-04
After that you should be able to run your Athena queries.