I'm in need to move my bigquery table to redshift.
Currently I have a python job that is fetching data from redshift, and it is incremental loading my data on the redshift.
This python job is reading bigquery data, creating a csv file in the server, drops the same on s3 and the readshift table reads the data from the file on s3. But now the time size would be very big so the server won't be able to handle it.
Do you guys happen to know anything better than this ?
The new 7 tables on bigquery I would need to move, is around 1 TB each, with repeated column set. (I am doing an unnest join to flattening it)
You could actually move the data from Big Query to a Cloud Storage Bucket by following the instructions here. After that, you can easily move the data from the Cloud Storage bucket to the Amazon s3 bucket by running:
gsutil rsync -d -r gs://your-gs-bucket s3://your-s3-bucket
The documentation for this can be found here
Related
Kinesis data firehose has a default format to add files into separate partitions in S3 bucket which looks like : s3://bucket/prefix/yyyy/MM/dd/HH/file.extension
I have created event streams to dump data from DynamoDB to S3 using Firehose. There is a transformation lambda in between which converts DDB records into TSV format (tab separated).
All of this is added on an existing table which already contains huge data. I need to backfill the existing data from DynamoDB to S3 bucket maintaining the parity in format with existing Firehose output style.
Solution I tried :
Step 1 : Export the Table to S3 using DDB Export feature. Use Glue crawler to create Data catalog Table.
Step 2 : Used Athena's CREATE TABLE AS SELECT Query to imitate the transformation done by the intermediate Lambda and storing that Output to S3 location.
Step 3 : However, Athena CTAS applies a default compression that cannot be done away with. So I wrote a Glue Job that reads from the previous table and writes to another S3 location. This job also takes care of adding the partitions based on year/month/day/hour as is the format with Firehose, and writes the decompressed S3 tab-separated format files.
However, the problem is that Glue creates Hive-style partitions which look like :
s3://bucket/prefix/year=2021/month=02/day=02/. And I need to match the firehose block style S3 partitions instead.
I am looking for an approach to help achieve this. Couldn't find a way to add block style partitions using Glue. Another approach I have is, to use AWS CLI S3 mv command to move all this data into separate folders with correct file-name which is not clean and optimised.
Leaving the solution I ended up implementing here in case it helps anyone.
I created a Lambda and added S3 event trigger on this bucket. The Lambda did the job of moving the file from Hive-style partitioned S3 folder to correctly structured block-style S3 folder.
The Lambda used Copy and delete function from boto3 s3Client to implement the same.
It worked like a charm even though I had like > 10^6 output files split across different partitions.
I have written a pyspark program that is reading data from cassandra and writing into aws s3 . Before writing into s3 I have to do repartition(1) or coalesce(1) as this creates one single file otherwise it creates multiple parquet files in s3 .
using repartition(1) or coalesce(1) has performance issue and I feel creating one big partition is not good option with huge data .
what are ways to create one single file in s3 but without compromising on performance ?
coalesce(1) or repartition(1) will put all your data on 1 partition (with a shuffle step when you use repartition compare to coalesce). In that case, only 1 worker will have to write all your data, which is the reason why you have performance issues - you already figured it out.
That is the only way you can use Spark to write 1 file on S3. Currently, there is no other way using just Spark.
Using Python (or Scala), you can do some other things. For example, you write all your files with spark without changing the number of partitions, and then :
you acquire your files with python
you concatenate your files as one
you upload that one file on S3.
It works well for CSV, not that well for non-sequential file type.
I'm trying to load a complete database dump into Redshift. Is there a single command to restore the data from a pg_dump living on s3 into Redshift? If not, what are the best steps for tackling this?
Thanks
If you have a non compressed pg_dump this should be possible using a psql command (you may need to manually edit to get the right syntax, depending on your versions and options set).
However this is a very inefficient and slow way to load redshift and I do not recommend it. If your tables are large it could take days or weeks!
What you need to do is this:
create target tables on redshift based upon the source table, but
considering sort keys and distribution.
unload you postgres source tables into csv files using postgres
"copy" command
If the source csv files are very big (e.g. more than say 100MB),
consider splitting these into separate files as they will load
faster (redshift will parallelize)
gzip the csv files (recommended but not essential)
upload these csv files to s3, with a separate folder per table
load the data into redshift from s3 by using the redshift copy
command
Guys a very basic question but not able to decipher ,Please help me out.
Q1: When we create bigquery table using below command , the data resides in same Cloud Storage?
bq load --source_format=CSV 'market.cust$20170101' \
gs://sp2040/raw/cards/cust/20170101/20170101_cust.csv
Q2: let's say my data director is gs://sp2040/raw/cards/cust/ for customer file Table structure defined is:
bq mk --time_partitioning_type=DAY market.cust \
custid:string,grp:integer,odate:string
Everyday I create new dir in the bucket such as 20170101,20170102..to load new dataset. So after the data loaded in this bucket Do I need to fire below queries.
D1:
bq load --source_format=CSV 'market.cust$20170101' \
gs://sp2040/raw/cards/cust/20170101/20170101_cust.csv
D2:
bq load --source_format=CSV 'market.cust$20170102' \
gs://sp2040/raw/cards/cust/20170102/20170102_cust.csv
When we create bigquery table using below command , the data resides in same Cloud Storage?
Nope! BigQuery is not using Cloud Storage for storing data (unless it is federated Table linked to Cloud Storage)
Check BigQuery Under the Hood with Tino Tereshko and Jordan Tigani - you will like it
Do I need to fire below queries
Yes. you need to load those files into BigQuery, so you can query the data
Yes you would need load the data into BigQuery using those commands.
However, there are a couple of alternatives
PubSub and Dataflow: You could configure PubSub to watch your cloud storage and create notification when files are added, described here. You could then have Dataflow job that imported the file into BigQuery. DataFlow Documentation
BigQuery external tables: BigQuery can query cvs files that are stored in Cloud Storage without importing the data, as described here. There is wildcard support for filenames so it could be configured once. Performance might not be as good as directly storing items in BigQuery
I have couple of spark jobs that produce parquet files in AWS S3. Every once in a while i need to run some ad-hoc queries on a given date range of this data. I don't want to do this in spark because I want our QA team which has no knowledge os spark be able to do this. What i like to do is to spin up an AWS EMR cluster and load the parquet files into HDFS and run my queries against it. I have figured out how to create tables with hive and point it to one s3 path. But then that limits my data to only one day. because each day of date has multiple files under a path like
s3://mybucket/table/date/(parquet files 1 ... n).
So problem one is to figure how to load multiple days of data into hive. ie
s3://mybucket/table_a/day_1/(parquet files 1 ... n).
s3://mybucket/table_a/day_2/(parquet files 1 ... n).
s3://mybucket/table_a/day_3/(parquet files 1 ... n).
...
s3://mybucket/table_b/day_1/(parquet files 1 ... n).
s3://mybucket/table_b/day_2/(parquet files 1 ... n).
s3://mybucket/table_b/day_3/(parquet files 1 ... n).
I know hive can support partitions but my s3 files are not setup that way.
I have also looked into prestodb which looks like to be the favorite tool for this type of data analysis. The fact it supports ansi SQL makes it a great tool for people that have SQL knowledge but know very little about hadoop or spark. I did install this on my cluster and it works great. But looks like you can't really load data into your tables and you have to rely on Hive to do that part. Is this the right way to use prestodb? I watched a netflix presentation about their use of prestodb and using s3 in place of HDFS. If this works its great but i wonder how the data is moved into memory. At what point the parquet files will be moved from s3 to the cluster. Do i need to have cluster that can load the entire data into memory? how is this generally setup?
You can install Hive and create Hive tables with you data in S3, described in the blog post here: https://blog.mustardgrain.com/2010/09/30/using-hive-with-existing-files-on-s3/
Then install Presto on AWS, configure Presto to connect the hive catalog which you installed previously. Then you can query the your data on S3, with Presto by using SQL.
Rather than trying to load multiple files, you could instead use the API to concatenate the days you want into a single object, which you can then load through the means you already mention.
AWS has a blog post highlighting how to do this exact thing purely through the API (without downloading + re-uploading the data):
https://ruby.awsblog.com/post/Tx2JE2CXGQGQ6A4/Efficient-Amazon-S3-Object-Concatenation-Using-the-AWS-SDK-for-Ruby