Current : Master1 - Standby1 (Streaming Replication)
Future : Master1 - Standby1
- Standby2 (For ETL, New VM)
Hi. We're using streaming replication now. I'm new with PostgreSQL.
I want to know If I add one more standby server, Is it possible to use that server for ETL?
I mean I'm gonna pause streaming in midnight for ETL in Standby2 and After ETL finished I'm gonna resume streaming.
I have no idea how long ETL takes. I found the content in the manual. WAL file will be received even though streaming stopped. But there's no more detail.
Question : If the Pause takes too long in standby2, Streaming replication could have a problem with Master1 and Standby1? I have to make sure there's no effect in our production system, though.
Question : If I resume streaming replication, Replication could be done automatically?
Question : Is there any best way to Make Data warehouse in PostgreSQL? I spend hours googling but I couldn't find documents what I really want.
Thank you:)
Related
The goal:
Real time CDC from Oracle and PostgreSQL to Kinesis on a single thread/process without much time lag and no record drop.
The system:
We have a system where we are doing a real time CDC from Oracle and PostgreSQL to Kinesis using AWS DMS.
The problem with doing a real time CDC with only one thread is that it takes many hours to replicate the changes to Kinesis when the data grows big(MBs).
Alternate approach:
The approach we took was to pull the real time changes from Oracle and PostgreSQL using multiple threads and push to Kinesis while still using DMS.
The challenge:
We noticed that while pulling data in real time using multiple threads, there is a drop in some records from Oracle and PostgreSQL. This happens in like 1 in 3 million records.
Tried different solutions on the Oracle and PostgreSQL side, talked to AWS and nothing works.
Notes:
We are using Logminner or Binary leader on Oracle and PostgreSQL side.
Is there a solution to this or has anybody tried to build this kind of system? Please let me know.
I am planning to migrate my production oracle cluster to postgresql cluster. Current systems support 2000TPS and in order to support that TPS, I would be very thankful if someone could clarify bellow.
1) What is the best replication strategy ( Streaming or DRBD based replication)
2) In streaming replication, can master process traffic without slave and when slave come up does it get what lost in down time ?
About TPS - it depends mainly on your HW and PostgreSQL configuration. I already wrote about it on Stackoverflow in this answer. You cannot expect magic on some notebook-like configuration. Here is my text "PostgreSQL and high data load application".
1) Streaming replication is the simplest and almost "painless" solution. So if you want to start quickly I highly recommend it.
2) Yes but you have to archive WAL logs. See below.
All this being said here are links I would recommend you to read:
how to set streaming replication
example of WAL log archiving script
But of course streaming replication has some caveats which you should know:
problem with increasing some parameters like max_connections
how to add new disk and new tablespace to master and replicas
There is no “best solution” in this case. Which solution you pick depends on your requirements.
Do you need a guarantee of no data lost?
How big a performance hit can you tolerate?
Do you need failover or just a backup?
Do you need PITR (Point In Time Recovery)?
By default, I think a failed slave will be ignored. Depending on your configuration, the slave might take a long time to recover after e.g. a boot.
I'd recommend you read https://www.postgresql.org/docs/10/static/different-replication-solutions.html
• What are ideal practices for taking PostgreSQL logical backup using pg_dump?
• Is it ideal to take backup from a standby/slave node? If replication lag is less than 200ms
• Is it ideal to take backup from standby/slave node, and is there any specific configuration we need to change?
• Which method is a good way for taking backups logical backup or physical backup? where DB is getting updated frequently. As a backup is taken for disaster recovery which method is the faster and better backup and disaster recovery(restore).
updated
Our current database size is 5GB and replication is on hot standby mode.
We are running the Backup script on slave node but it takes remote backup from the master node every 30 minutes.
The reason I created this question is to understand when the backup is running some COPY statements takes 6 mins to complete, even though it will not affect other transactions on DB, is there any other issues occurs if a statement is taking more time.
I thought about what you wrote and here are some ideas for you:
If you need backup which will really be consistent to some point in time then you must use pg_basebackup or pg_barman (internally uses pg_basebackup) - explanation is in 1. link below. Latest pg_basebackup 10 streams WAL logs so you backup also all changes done during backup. Of course this backup takes only the whole PG instance. On the other hand it does not lock any table. And if you do it from remote instance then it causes only small CPU load on PG instance and disk IO is not as big as some texts suggests. See links 4 about my experiences. Restoration is quite simple - see link 5.
If you use pg_dump you must understand that you have no guarantee that your backup is really consistent to the point in time - again see link 1. There is a possibility to use snapshot of the database (see links 2 and 3) but even with it you cannot count on 100% consistency. We used pg_dump only on our analytical database which loads new only 1x per day (yesterdays partitions from production database). You can speed it with parallel option (works only for directory backup format). But downside is much higher load on PG instance - higher CPU usage, much higher disk IO. Even if you run pg_dump remotely - in such case you save only disk IO for saving of backup files. Plus pg_dump needs to place read lock on tables so it can collied either with new inserts or with replication (when taken on replica). But when your database reaches hundreds of GBs then even parallel dump can takes hours and in that moment you would need to switch to pg_basebackup anyway.
pg_barman is "comfortable version" of pg_basebackup + it allows you to prevent data loss even when your PG instance crashes very badly. Setting it to work requires more changes but it is definitely worth it. You will have to set WAL log archiving (see link 6) and if you PG is <10 you will have to set "max_wal_senders" and "max_replication_slots" (which you need for replication anyway) - everything is in pg-barman manual although description is not exactly great. pg_barman will stream and store WAL records even between backups so this way you can be sure that data loss in case of very bad crash will be almost none. But making it work can take many hours because descriptions are not exactly good. pg-barman does both backup and restoration with its commands.
Your database is 5GB big so any backup method will be quick. But you have to decide if you need point in time recovery and almost zero data loss or not - so if you will invest time to setting pg-barman or not.
Links:
PostgreSQL, Backups and everything you need to know
Review for Paper: 14-Serializable Snapshot Isolation in PostgreSQL - about snapshots
Parallel dumping of databases - example how to use snapshot
pg_basebackup experiencies
pg_basebackup - restore tar backup
Archiving WAL logs using script
I have noticed that spark streaming examples also have code for checkpointing. My question is how important is that checkpointing. If its there for fault tolerance, how often do faults happen in such streaming applications?
It all depends on your use case. For suppose if you are running a streaming job, which just reads data from Kafka and counts the number of records. What would you do if your application crashes after a year or so?
If you don't have a backup/checkpoint, you will have to recompute all the previous one years worth data so you can resume counting.
If you have a backup/checkpoint, you can simply read the checkpoint data and resume instantly.
Or if all you are just doing is having a streaming application which just Reads-Messages-From-Kafka >>> Tranform >>> Insert-to-a-Database, I need not worry about my application crashing. Even if it's crashed, i can simply resume my application without loss of data.
Note: Check-pointing is a process which stores the current state of a spark application.
Coming to the frequency of fault tolerance, you can almost never predict an outage. In companies,
There might be power outage
regular maintainance/upgrading of cluster
hope this helps.
There are two cases:
You are doing stateful operations, such as updateStateByKey, then
you must use checkpointing - every state is saved. Without setting
checkpoint directory, an exception will be thrown.
You are doing only windowed operations - then yes, you can disable checkpointing. However I strongly recommend setting checkpoint directory.
When driver is killed, then you'll loose all your data and progress information. Checkpointing helps you to recover applications from such situations.
Is a failure a normal situation? Of course! Imagine that you've got large cluster, many machines, many components in these machines. If one of these components fails, then your application will also fail. When connection to driver is lost - your application fails. With checkpoiting you can just run application again and it will recover state.
Amazon says the following on Redshift billing
"Node usage hours are billed for each hour your data warehouse cluster is running in an Available state. If you no longer wish to be charged for your data warehouse cluster, you must terminate it to avoid being billed for additional node hours."
This means if I just create a cluster and whether use it or not I'll be billed 24/7 because the cluster doesn't have any state like "Suspend". Is there a way to shut down the whole Redshift server when not in use so that I'll be billed only for the hours when I want to use the clusters?
Edit: With Tomasz's reply it sounds like if I want to shutdown the cluster on weekend it'll be like backing up the whole database on Friday evening and restoring on Sunday evening. This doesn't sound good. What does Amazon really mean when they say "PAY ONLY FOR THE HOURS YOU USE"?
Can you tell me how much time will it take to backup/restore a data warehouse of size around 100GB? Can I automatically associate security groups to the cluster after restoring from the Java code?
You can create a manual snapshot of a cluster when you have finished work and then remove cluster.
You will pay for S3 storage, but that is much less than for running Redshift cluster.
Next day just restore cluster from latest snapshot. You will have to add security groups to new cluster, probably with JAVA API:
The new cluster will be associated only with the default security and
parameter groups. If the original cluster was associated with any
other security or parameter group, you will need to manually associate
those groups with the new cluster.
The easiest way to create snapshot is from the console, but you probably will want to do it automatically using cli or Java SDK.
Creating a snapshot of a 3 node cluster filled up to 80% took me about 5 minutes (it's so quick because snapshots are incremental). 100GB is much less than my setup, so it should be even faster. Also restore shouldn't take long time.
UPDATE: A lot has changed in the intervening years, in particular restore from snapshot is now quite fast. Your cluster becomes available in a few minutes and you can run queries while the restore continues in the background. Total time for complete restore of 100GB would now be measured in minutes (varies based on node type & count).
What does Amazon really mean when they say "PAY ONLY FOR THE HOURS YOU USE"?
You pay for the whole hour of any partial hours used.
Can you tell me how much time will it take to backup/restore a data warehouse of size around 100GB?
Snapshots are incremental and this is what makes them fast (as Tomasz mentioned). It's is fairly quick to shutdown a cluster about half an hour. However restoring from a snapshot is very slow I'd suggest around 3 hours for restoring 100GB.
If you really want to be able to take a database cluster up and down quickly you might be better using another analytic DB (e.g. Greenplum or Vertica free editions) with the data stored on EBS volumes. It'd be a lot more work to manage though, that's the tradeoff.
Now we can able to pause and resume the Redshift cluster (both Console and CLI)
check out the link:
https://aws.amazon.com/blogs/big-data/lower-your-costs-with-the-new-pause-and-resume-actions-on-amazon-redshift/