I a trying to do the migration for our Postgres database to Aurora postgres
first I create a normal task it migrates all tables only except its constraints.
My tries to clone our database
I downloaded AWS SCT (Schema Conversion Tool) then set my configuration to generate a migration report, here is the report
We completed the analysis of your PostgreSQL source database and
estimate that 100% of the database storage objects and 99.1% of
database code objects can be converted automatically or with minimal
changes if you select Amazon Aurora (PostgreSQL compatible) as your
migration target. Database storage objects include schemas, tables,
table constraints, indexes, types, sequences and foreign tables.
Database code objects include triggers, views, materialized views,
functions, domains, rules, operators, collations, fts configurations,
fts dictionaries and aggregates. Based on the source code syntax
analysis, we estimate 99.9% (based on # lines of code) of your code
can be converted to Amazon Aurora (PostgreSQL compatible)
automatically. To complete the migration, we recommend 133 conversion
action(s) ranging from simple tasks to medium-complexity actions to
complex conversion actions.
my question:
1- is there a way to automate including everything in my source database
2- the report mentions we recommend 133 conversion action(s) where I can find these conversion actions.
3- is it safe to ongoing migration as in my case we need to run migration every day.
Sequence, Index, and Constraint are not migrated and it is mentioned in the official docs on AWS.
You can use this source.
This will help you to migrate Sequence, Index, and Constraint at once.
p.s: this doesn't include View and Routine.
There's no way AFAIK in AWS to automate everything if that was there then it would have been already added in SCT. however, if there are similar errors that are occurring in code/DDL/function like some datatype conversions. you can create a script that will take schema dump and convert all these data types to the desired ones.
Choose the SQL Conversion Actions tab in SCT tool.
The SQL Conversion Actions tab contains a list of SQL code items that can't be converted automatically. There are also recommendations for how to manually convert the SQL code. You can look into the errors and make changes accordingly.
In case if you are migrating to the same version of PG in aurora you can take a schema only dump and restore it into target aurora and later setup a full load/ongoing replication with DMS and you don't have to take SCT into consideration(most of the time worked for me). Just make sure you adhere to aurora limitations specific to the PG version
We have been using ongoing migration in our project at it's working great. There are some best practices we have developed but that will differ from project to project
DDL changes must be made on the target first and stop replication while doing it and resume once done
Separate the tables with high transactions as different DMS task as it will help you in troubleshooting and your rest of the tables can still be working
Always keep in mind DMS replicates data, not the view/function/procedures
Active monitoring of tasks and replication instances
And I would like to suggest if you are performing homogenous migration(PG -> PG) you should consider pg_dump & pg_restore that easy and sophisticated for the same versions and AWS aurora supports it.
Related
How to migrate my whole database which is currently in AWS RDS Postgres to AWS Redshift and also can you please help me out how can I keep both these DBs in sync. I want to sync even if any column is updated in RDS so it must get updated in Redshift also.
I know we can achieve it with AWS Glue, but the above scenario is mandatory in my case. Migration task is easy to do but to to the CDC migration is bit challenging. I am also aware about the bookmark key but my situation is bit different, I do not have any sequential column in the tables, but it has updated_at field in all the tables so this column is the only field on which I can check whether the record is processed or not so that duplicate processing may not occur and if any new data is inserted it should also get replicated in RedShift.
So, would anyone help me out to do this even by using pyspark script?
Thanks.
I am trying to make a replication server from MySQL to redshift, for this, I am parsing the MySQL binlog. For initial replication, I am taking the dump of the mysql table, converting it into a CSV file and uploading the same to S3 and then I use the redshift copy command. For this the performance is efficient.
After the initial replication, for the continuous sync when I am reading the binlog the inserts and updates have to be run sequentially which are very slow.
Is there anything that can be done for increasing the performance?
One possible solution that I can think of is to wrap the statements in a transaction and then send the transaction at once, to avoid multiple network calls. But that would not address the problem that single update and insert statements in redshift run very slow. A single update statement is taking 6s. Knowing the limitations of redshift (That it is a columnar database and single row insertion will be slow) what can be done to work around those limitations?
Edit 1:
Regarding DMS: I want to use redshift as a warehousing solution which just replicates our MYSQL continuously, I don't want to denormalise the data since I have 170+ tables in mysql. During ongoing replication, DMS shows many errors multiple times in a day and fails completely after a day or two and it's very hard to decipher DMS error logs. Also, When I drop and reload tables, it deletes the existing tables on redshift and creates and new table and then starts inserting data which causes downtime in my case. What I wanted was to create a new table and then switch the old one with new one and delete old table
Here is what you need to do to get DMS to work
1) create and run a dms task with "migrate and ongoing replication" and "Drop tables on target"
2) this will probably fail, do not worry. "stop" the dms task.
3) on redshift make the following changes to the table
Change all dates and timestamps to varchar (because the options used
by dms for redshift copy cannot cope with '00:00:00 00:00' dates that
you get in mysql)
change all bool to be varchar - due to a bug in dms.
4) on dms - modify the task to "Truncate" in "Target table preparation mode"
5) restart the dms task - full reload
now - the initial copy and ongoing binlog replication should work.
Make sure you are on latest replication instance software version
Make sure you have followed the instructions here exactly
http://docs.aws.amazon.com/dms/latest/userguide/CHAP_Source.MySQL.html
If your source is aurora, also make sure you have set binlog_checksum to "none" (bad documentation)
I'm trying to migrate our database engine from MsSql to PostgreSQL. In our automated test, we restore the database back to "clean" state at the start of every test. We do this by comparing the "diff" between the working copy of the database with the clean copy (table by table). Then copying over any records that have changed. Or deleting any records that have been added. So far this strategy seems to be the best way to go about for us because per test, not a lot of data is changed, and the size of the database is not very big.
Now I'm looking for a way to essentially do the same thing but with PostgreSQL. I'm considering doing the exact same thing with PostgreSQL. But before doing so, I was wondering if anyone else has done something similar and what method you used to restore data in your automated tests.
On a side note - I considered using MsSql's snapshot or backup/restore strategy. The main problem with these methods is that I have to re-establish the db connection from the app after every test, which is not possible at the moment.
If you're okay with some extra storage, and if you (like me) are particularly not interested in re-inventing the wheel in terms of checking for diffs via your own code, you should try creating a new DB (per run) via templates feature of createdb command (or CREATE DATABASE statement) in PostgreSQL.
So for e.g.
(from bash) createdb todayDB -T snapshotDB
or
(from psql) CREATE DATABASE todayDB TEMPLATE snaptshotDB;
Pros:
In theory, always exact same DB by design (no custom logic)
Replication is a file-transfer (not DB restore). So far less time taken (i.e. doesn't run SQL again, doesn't recreate indexes / restore tables etc.)
Cons:
Takes 2x the disk space (although template could be on a low performance NFS etc)
For my specific situation. I decided to go back to the original solution. Which is to compare the "working" copy of the database with "clean" copy of the database.
There are 3 types of changes.
For INSERT records - find max(id) from clean table and delete any record on working table that has higher ID
For UPDATE or DELETE records - find all records in clean table EXCEPT records found in working table. Then UPSERT those records into working table.
As there is no support for user defined functions or stored procedures in RedShift, how can i achieve UPSERT mechanism in RedShift which is using ParAccel, a PostgreSQL 8.0.2 fork.
Currently, i'm trying to achieve UPSERT mechanism using IF...THEN...ELSE... statement
e.g:-
IF NOT EXISTS(SELECT...WHERE(SELECT..))
THEN INSERT INTO tblABC() SELECT... FROM tblXYZ
ELSE UPDATE tblABC SET.,.,.,. FROM tblXYZ WHERE...
which is giving me error. As i'm writing this code independently without including it in function or SP's.
So, is there any solution to achieve UPSERT.
Thanks
You should probably read this article on upsert by depesz. You can't rely on SERIALIABLE for this since, AFAIK, ParAccel doesn't support full serializability support like in Pg 9.1+. As outlined in that post, you can't really do what you want purely in the DB anyway.
The short version is that even on current PostgreSQL versions that support writable CTEs it's still hard. On an 8.0 based ParAccel, you're pretty much out of luck.
I'd do a staged merge. COPY the new data to a temporary table on the server, LOCK the destination table, then do an UPDATE ... FROM followed by an INSERT INTO ... SELECT. Doing the data uploads in big chunks and locking the table for the upserts is reasonably in keeping with how Redshift is used anyway.
Another approach is to externally co-ordinate the upserts via something local to your application cluster. Have all your tools communicate via an external tool where they take an "insert-intent lock" before doing an insert. You want a distributed locking tool appropriate to your system. If everything's running inside one application server, it might be as simple as a synchronized singleton object.
Is it possible to have a MS access backend database (Microsoft JET or Access Database Engine) set up so that whenever entries are inserted/updated those changes are replicated* to a PostgreSQL database?
Two-way synchronization would be nice, but one way would be acceptable.
I know it's popular to link the two and use one as a frontend, but it's essential that both be backend.
Any suggestions?
* ie reflected, synchronized, mirrored
Can you use Microsoft SQL Server Express Edition? Or do you have to use Microsoft Access Database Engine? It's possible you'll have more options using MS SQL express, like more complete triggers and logging.
Either way, you're going to need a way to accumulate a log of changed rows from the source database engine, and a program to sync them to PostgreSQL by reading the log and converting it into suitable PostgreSQL INSERT, UPDATE and DELETE statements.
You could do this by having audit triggers in MADB/Express insert a row into an audit shadow table for every "real" table whenever it changed, including inserting special "row deleted" audit entries. Then your sync program could connect to both MADB/Express, read the audit tables, apply the changes to PostgreSQL, and empty the audit tables.
I'll be surprised if you find anything to do this out of the box. It's one area where Microsoft SQL Server has a big advantage because of all the deep Access and MADB engine integation to support the synchronisation and integration features.
There are some ETL ("Extract, Transform, Load") tools that might be helpful, like Pentaho and Talend. I don't know if you can achieve the desired degree of automation with them though.