I want to import a large csv file (around 1gb with 2.5m rows and 50 columns) into a DynamoDb, so have been following this blog from AWS.
However it seems I'm up against a timeout issue. I've got to ~600,000 rows ingested, and it falls over.
I think from reading the CloudWatch log that the timeout is occurring due to the boto3 read on the CSV file (it opens the entire file first, iterates through and batches up for writing)... I tried to reduce the file size (3 columns, 10,000 rows as a test), and I got a timeout after 2500 rows.
Any thoughts here?!
TIA :)
I really appreciate the suggestions (Chris & Jarmod). After trying and failing to break things programmatically into smaller chunks, I decided to look at the approach in general.
Through research I understood there were 4 options:
Lambda Function - as per the above this fails with a timeout.
AWS Pipeline - Doesn't have a template for importing CSV to DynamoDB
Manual Entry - of 2.5m items? no thanks! :)
Use an EC2 instance to load the data to RDS and use DMS to migrate to DynamoDB
The last option actually worked well. Here's what I did:
Create an RDS database (I used the db.t2.micro tier as it was free) and created a blank table.
Create an EC2 instance (free Linux tier) and:
On the EC2 instance: use SCP to upload the CSV file to the ec2 instance
On the EC2 instance: Firstly Sudo yum install MySQL to get the tools needed, then use mysqlimport with the --local option to import the CSV file to the rds MySQL database, which took literally seconds to complete.
At this point I also did some data cleansing to remove some white spaces and some character returns that had crept into the file, just using standard SQL queries.
Using DMS I created a replication instance, endpoints for the source (rds) and target (dynamodb) databases, and finally created a task to import.
The import took around 4hr 30m
After the import, I removed the EC2, RDS, and DMS objects (and associated IAM roles) to avoid any potential costs.
Fortunately, I had a flat structure to do this against, and it was only one table. I needed the cheap speed of the dynamodb, otherwise, I'd have stuck to the RDS (I almost did halfway through the process!!!)
Thanks for reading, and best of luck if you have the same issue in the future.
After we changed the dynamodb table capacity to on-demand, the data pipeline job to export dynamodb table failed with this error.
Exception in thread "main" java.lang.RuntimeException: Read throughput should not be less than 1. Read throughput percent: 0.0
at org.apache.hadoop.dynamodb.read.AbstractDynamoDBInputFormat.getSplits(AbstractDynamoDBInputFormat.java:51)
at org.apache.hadoop.mapreduce.JobSubmitter.writeOldSplits(JobSubmitter.java:520)
at org.apache.hadoop.mapreduce.JobSubmitter.writeSplits(JobSubmitter.java:512)
at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:394)
Any workaround to this issue?
Thanks
--gsu
I'd contact AWS support to confirm, but I was told the EMR DynamoDB connector does not formally support tables using on-demand provisioning yet. So, more than likely you need to switch the table back to provisioned capacity as a workaround.
Edit: As of 23 January 2019, the EMR connector for DynamoDB supports tables set to on-demand billing.
If the issue is not resolved, then you might need to change at 3 places:
You need to use emr release emr-5.26.0 to emr-5.30.0.
Replace org.apache.hadoop.dynamodb.tools.DynamoDbExport with org.apache.hadoop.dynamodb.tools.DynamoDBExport. Notice the change in casing. Similar for DynamoDBImport.
If you are using emr-dynamodb-connector, then you need to clone its latest version, generate the emr-ddb-tools jar by mvn clean install and then use the generated jar for emr-dynamodb-tools which would be version 4 or higher currently and replace this in your arguments. These should resolve the issue.
Also, there is an issue currently with emr releases 31 or higher if you are using emr-dynamodb-tools which shows you error related to some joda-time framework. I would restrict to using any releases between emr-5.26.0 to emr-5.30.0.
Question from Redshift newbies: I copy data using AWS datapipeline but it FAILED and log said
"ERROR: Disk Full Detail:
----------------------------------------------- error: Disk Full code: 1016 context: node: 0 query: 2070045 location: fdisk_api.cpp:343
process: query0_49 [pid=15048] "
I'd like to know how could we check if Redshift is really disk full via CLI or web console, any comments or hints would be appreciated.
If you're using a single node and have SQL access to the cluster (e.g. via psql), you can run:
select
sum(capacity)/1024 as capacity_gbytes,
sum(used)/1024 as used_gbytes,
(sum(capacity) - sum(used))/1024 as free_gbytes
from
stv_partitions where part_begin=0;
This articles has more: https://www.flydata.com/blog/querying-free-disk-space-on-redshift/
You can check that in CloudWatch console. In the left bar, you'll see bunch of AWS services under the 'Metrics' heading. Click on Redshift. In that, look for the 'PercentageDiskSpaceUsed' metric for the concerned cluster.
Also, do remember that this metric is separately available for each compute node.
I've tried default setup of Data Pipeline for cross-regional table copy. Copying one table to another in same region (eu-west-1).
On pipeline activation, EMR cluster is launched, runs for approx 20 minutes and then it's terminated with pipeline being in "success" state.
Problem is that only 389 entries are copied from my table :/ (number is the same on multiple runs). Total number of entries is close to 100000.
I've tried turning on logs (no errors there), walking through them, launching 4.1.0 cluster, increasing throughputs, etc, nothing solves the case.
Does cross-regional table copy work? What could be the problem? Why no error? How do I debug it?
Datapipeline config: https://gist.github.com/mariusgrigaitis/adceb18354b52d845278
I am trying to setup Replica outside of AWS and master is running at AWS RDS. And I do not want any downtime at my master. So I setup my slave node and now I want to backup my current database which is at AWS.
mysqldump -h RDS ENDPOINT -u root -p --skip-lock-tables --single-transaction --flush-logs --hex-blob --master-data=2 --all-databases > /root/dump.sql
I tested it on my VM and it worked fine but when tying it with RDS it gives me error
mysqldump: Couldn't execute 'FLUSH TABLES WITH READ LOCK': Access denied for user 'root'#'%' (using password: YES) (1045)
Is it because i do not have super user privilege or how to I fix this problem? Please someone suggest me.
RDS does not allow even the master user the SUPER privilege, and this is required in order to execute FLUSH TABLES WITH READ LOCK. (This is an unfortunate limitation of RDS).
The failing statement is being generated by the --master-data option, which is, of course, necessary if you want to be able to learn the precise binlog coordinates where the backup begins. FLUSH TABLES WITH READ LOCK acquires a global read lock on all tables, which allows mysqldump to START TRANSACTION WITH CONSISTENT SNAPSHOT (as it does with --single-transaction) and then SHOW MASTER STATUS to obtain the binary log coordinates, after which it releases the global read lock because it has a transaction that will keep the visible data in a state consistent with that log position.
RDS breaks this mechanism by denying the SUPER privilege and providing no obvious workaround.
There are some hacky options available to properly work around this, none of which may be particularly attractive:
do the backup during a period of low traffic. If the binlog coordinates have not changed between the time you start the backup and after the backup has begin writing data to the output file or destination server (assuming you used --single-transaction) then this will work because you know the coordinates didn't change while the process was running.
observe the binlog position on the master right before starting the backup, and use these coordinates with CHANGE MASTER TO. If your master's binlog_format is set to ROW then this should work, though you will likely have to skip past a few initial errors, but should not have to subsequently have any errors. This works because row-based replication is very deterministic and will stop if it tries to insert something that's already there or delete something that's already gone. Once past the errors, you will be at the true binlog coordinates where the consistent snapshot actually started.
as in the previous item, but, after restoring the backup try to determine the correct position by using mysqlbinlog --base64-output=decode-rows --verbose to read the master's binlog at the coordinates you obtained, checking your new slave to see which of the events must have already been executed before the snapshot actually started, and using the coordinates determined this way to CHANGE MASTER TO.
use an external process to obtain a read lock on each and every table on the server, which will stop all writes; observe that the binlog position from SHOW MASTER STATUS has stopped incrementing, start the backup, and release those locks.
If you use any of these approaches other than perhaps the last one, it's especially critical that you do table comparisons to be certain your slave is identical to the master once it is running. If you hit subsequent replication errors... then it wasn't.
Probably the safest option -- but also maybe the most annoying since it seems like it should not be necessary -- is to begin by creating an RDS read replica of your RDS master. Once it is up and synchronized to the master, you can stop replication on the RDS read replica by executing an RDS-provided stored procedure, CALL mysql.rds_stop_replication which was introduced in RDS 5.6.13 and 5.5.33 which doesn't require the SUPER privilege.
With the RDS replica slave stopped, take your mysqldump from the RDS read replica, which will now have an unchanging data set on it as of a specific set of master coordinates. Restore this backup to your off-site slave and then use the RDS read replica's master coordinates from SHOW SLAVE STATUS Exec_Master_Log_Pos and Relay_Master_Log_File as your CHANGE MASTER TO coordinates.
The value shown in Exec_Master_Log_Pos on a slave is the start of the next transaction or event to be processed, and that's exactly where your new slave needs to start reading on the master.
Then you can decommission the RDS read replica once your external slave is up and running.
Thanks Michael, I think the most correct solution and the recommended by AWS is do the replication using a read replica as a source as explained here.
Having a RDS master, RDS read replica and an instance with MySQL ready, the steps to get an external slave are:
On master, increase binlog retention period.
mysql> CALL mysql.rds_set_configuration('binlog retention hours', 12);
On read replica stop replication to avoid changes during the backup.
mysql> CALL mysql.rds_stop_replication;
On read replica annotate the binlog status (Master_Log_File and Read_Master_Log_Pos)
mysql> SHOW SLAVE STATUS;
On server instance do a backup and import it (Using mydumper as suggested by Max can speed up the process).
mysqldump -h RDS_READ_REPLICA_IP -u root -p YOUR_DATABASE > backup.sql
mysql -u root -p YOUR_DATABASE < backup.sql
On server instance set it as slave of RDS master.
mysql> CHANGE MASTER TO MASTER_HOST='RDS_MASTER_IP',MASTER_USER='myrepladmin', MASTER_PASSWORD='pass', MASTER_LOG_FILE='mysql-bin-changelog.313534', MASTER_LOG_POS=1097;
Relace MASTER_LOG_FILE and MASTER_LOG_POS to the values of Master_Log_File Read_Master_Log_Pos you saved before, also you need an user in RDS master to be used by slave replication.
mysql> START SLAVE;
On server instance check if replication was success.
mysql> SHOW SLAVE STATUS;
On RDS read replica resume replication.
mysql> CALL mysql.rds_start_replication;
for RDS binlog position you can use mydumper with --lock-all-tables, it will use LOCK TABLES ... READ just to get the binlog coordinates and then realease it instead of FTWRL.
Michael's answer is extremely helpful and focuses on the main sticking point: you simply cannot GRANT the required SUPER privilege on RDS, and therefore you can't use the --master-data flag that would make things so much easier.
I read that it may be possible to work around this by creating or modifying a Database Parameter Group via the API, but I think using the RDS procedures is a better option.
The multi-tiered replication approach works well, though, and can include tiers outside RDS/VPC so it's possible to replicate from "Classic" EC2 to VPC using this method.
A lot of the necessary functionality is only in later releases of MySQL 5.5 and 5.6, and I strongly recommend you run the same version on all the DBs involved in the replication stack, so you may have to do an upgrade of your old DB before all of this, which means yet more tedium and replication and so on.
I had faced a similar problem a quick workaround to this is:
Create a EBS Volume to have an extra space or extend current EBS volume on EC2. (or if you have an extra space you can use that).
Use mysqldump command without --master-data or --flush-data directive to generate a complete (FULL) backup of db.
mysqldump -h hostname --routines -uadmin -p12344 test_db > filename.sql
admin is DB name and 12344 is the password
Above is for taking backup of one single DB, if required to take all DBs then specify --all-databases and also mention DB Names.
Create a Cron of this command to run once a day that will automatically generate the dump.
Please note that this will incur an extra cost if your DB Size is huge. as it creates a complete DB dump.
hope this helps
You need to
1- create a read replica on aws
2- make sure that this instance is catching up with the master
3- stop the replication and get the log_file and log_position parameters by
show slave status \G
4- dump the database and use the parameters logged in step 3 to start the replication on your own server.
5- start the slave.
Detailed instructions here
Either things have changed, since #Michael - sqlbot 's response, or there is a misunderstanding going on here (could be on my part),
You can use COPY to import a csv file into rds, at least on the postgres version, you just need to use FROM STDIN instead of directly naming the file,
which means you end up piping things like:
cat data.csv | psql postgresql://server:5432/mydb -U user -c "COPY \"mytable\" FROM STDIN DELIMITER ',' "