In the API of DynamoDB there is a way to increase/decrease table Provisioned Throughput but there is some Active mode that needs to be updated, what if there is two scripts that running on the same table at the same time and one of them read and the other update the table, What's going to happened with the one that reading? Is it going to failed?
I think maybe before every reading I can check if the table is on Active mode and if not just wait until it does but each time that I'm Query/Scan the database I need to make this check. Maybe it's not necessary.
Is anyone know about this?
It's not necessary, you can still read from the table when it's been updated.
EDIT:
from http://aws.amazon.com/dynamodb/faqs/
Q: Does Amazon DynamoDB remain available when I ask it to scale up or down by changing the provisioned throughput?
Yes. Amazon DynamoDB is designed to scale its provisioned throughput up or down while still remaining available.
DynamoDB reads are "eventually consistent", so the query/scan may not see the updated rows but the request will not fail. You can request consistent reads if you need them though (though they consume slightly more Read Capacity).
See the docs for more information.
Related
I am developing an application using DynamoDB. This application is not yet open to the public so only certain employees can access the application.
Generally, the application is very fast and there are no performance issues. Sometimes, however, the application is extremely slow.
At first I suspected that the problem comes from React JS application or from the API but that problem is from DynamoDB.
How can I affirm this?
I tested by stopping Node JS (so the API was offline)
I tested directly in the AWS console in "Explore table items" screens and in "PartiQL editor" screens
And DynamoDB was very very slow and I get this error:
The level of configured provisioned throughput for one or more global secondary indexes of the table was exceeded.
Consider increasing your provisioning level for the under-provisioned global secondary indexes with the UpdateTable API
I cannot understand because no application is running.
So why DynamoDB because slow ?
---> Maybe there is a bug in the API. Engineer are works on that.
But why does the DynamoDB keep running slow when API was offline?
How can I "restart" and/or "stop" DynamoDB service?
Best regards
Update: 2022-09-05 17h42 (Japan Time)
I created two videos to illustrate what I say (Sorry for the delay because to create the videos I had to wait for the database bugs):
Normal Case: DynamoDB is very very fast
https://youtu.be/ayeccV0zk0E
Issue Case: DynamoDB is very very slow
https://youtu.be/1u201N2HV8o
---> On my example, I have only 52 Users so this is bug not normal.
Regards
The error message is giving you a potential cause for your perceived slowness.
I suspect that what you perceive as slowness is because the throughput of the Global Secondary Index your app is reading from is exhausted, and the app (or the AWS SDK) is performing exponential backoff to retry the API call.
The one dimension you scale DynamoDB with aside from the Key schema is Throughput. You decide how many requests per second (it's a bit more complicated than that) DynamoDB can handle, and AWS ensures that load can be served. If you go beyond that, AWS throttles API calls, and you receive the errors.
GSIs have their own throughput that you can manage. I suggest you take a look at the provided metrics to identify where your throughput bottleneck is and adjust the throughput accordingly. If you don't want to deal with throughput at all, switch the table to On-Demand Capacity (Pay per request) and AWS handles that for you at a small premium.
The error message mentions provisioned throughput of a GSI, so it is quite likely that this is your problem:
The DynamoDB GSI documentation https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/GSI.html#GSI.ThroughputConsiderations explains that
When you create a global secondary index on a provisioned mode table, you must specify read and write capacity units for the expected workload on that index. The provisioned throughput settings of a global secondary index are separate from those of its base table. A Query operation on a global secondary index consumes read capacity units from the index, not the base table. When you put, update or delete items in a table, the global secondary indexes on that table are also updated. These index updates consume write capacity units from the index, not from the base table.
For example, if you accidentally set a GSI's read provisioning to 1, then you can only do on average one read per second from this GSI. If you do a scan that needs to return 10 items, it may take around 10 seconds to complete. Even if no other application is using the table.
Please read the aforementioned link for the full story on how to provision secondary indexes in DynamoDB.
If this is not your problem, please update your question with details on the provisioned throughput settings of your base table and its GSI.
I am working on an application which receives very predictable, heavy traffic during working hours. Users typically interact with the app for about 40 minutes at a time. DynamoDB table A receives a steady stream of writes throughout user sessions and handles things without difficulty. We attempt to write a large amount of data to table B at the end of each session, however, and early in the day this can result in throttling. Our tables are billed on-demand (no, this is not something I am able to change), but the sudden spike in writes still causes throttling, which is expected.
The data being written to table A is both critical and time sensitive. The data going to table B is critical and must not be lost, but delays in data availability from table B on the order of a few hours is acceptable, but not ideal. So I'm looking for a way to say "please write this to the table ASAP, but only as long as it won't cause throttling". Provisioning for the expected capacity is not an option (don't ask). An SQS queue with a long message delay doesn't really fit the bill because (a) 15 minutes may not be long enough and (b) it doesn't meet the "ASAP" part of the story. I've considered pre-warming the table, but that's just cludgy.
So... you take all the expected ways to handle this that were designed and provided by AWS then say you can't use them. That... doesn't leave you much options.
You're pretty much left with designing some custom architecture. Throttling, provisioning, burst provisioning, on demand, and all are all part of the package for handling these kinds of bursts. If you can't use them, then you'll have to do something like write the entry as a json to an s3 bucket and have some cron event pick them up in an hour or something one a time and batch write them to the table.
You may want to take a look at how your table is arranged. If you are having to make a lot of writes all at once (ie, because you have to duplicate data through multiple PK/SK combinations in order to be able to recall it with a single query) then an RDS may be better suited for the task at hand. Dynamo is more for quick and snappy queries and not really for extended data logging or storage.
Here's the secret to DDB on-demand...
From the page you linked to
For new on-demand tables, you can immediately drive up to 4,000 write
request units or 12,000 read request units, or any linear combination
of the two. For an existing table that you switched to on-demand
capacity mode, the previous peak is half the previous provisioned
throughput for the table—or the settings for a newly created table
with on-demand capacity mode, whichever is higher. For more
information, see Initial throughput for on-demand capacity mode.
And the Inital throughput for on-demand capacity mode page says:
Initial Throughput for On-Demand Capacity Mode If you recently
switched an existing table to on-demand capacity mode for the first
time, or if you created a new table with on-demand capacity mode
enabled, the table has the following previous peak settings, even
though the table has not served traffic previously using on-demand
capacity mode:
Newly created table with on-demand capacity mode: The previous peak is
2,000 write request units or 6,000 read request units. You can drive
up to double the previous peak immediately, which enables newly
created on-demand tables to serve up to 4,000 write request units or
12,000 read request units, or any linear combination of the two.
Existing table switched to on-demand capacity mode: The previous peak
is half the maximum write capacity units and read capacity units
provisioned since the table was created, or the settings for a newly
created table with on-demand capacity mode, whichever is higher. In
other words, your table will deliver at least as much throughput as it
did prior to switching to on-demand capacity mode.
The key thing to realize is that DDB on-demand "peaks" are never lowered..
So if you have a table that at some point peaked at 20K WCU, you can scale cleanly from 1-20K without throttling.
In other words, you shouldn't continue to see throttling in an app unless you hit a new peak.
You can also artificially set the peak by changing the table to provisioned at double the expected peak. Then when you convert it back to on-demand, you'll have a "peak" set for half the provisioned capacity.
I have an Amazon DynamoDB table which is used for both read and write operations. Write operations are performed only when the batch job runs at certain intervals whereas Read operations are happening consistently throughout the day.
I am facing a problem of increased Read latency when there is significant amount of write operations are happening due to the batch jobs. I explored a little bit about having a separate read replica for DynamoDB but nothing much of use. Global tables are not an option because that's not what they are for.
Any ideas how to solve this?
Going by the Dynamo paper, the concept of a read-replica for a record or a table does not exist in Dynamo. Within the same region, you will have multiple copies of a record depending on the replication factor (R+W > N) where N is the replication factor. However when the client reads, one of those records are returned depending on the cluster health.
Depending on how the co-ordinator node is chosen either at the client library or at the cluster, the client can only ask for a record (get) or send a record(put) to either the cluster co-ordinator ( 1 extra hop ) or to the node assigned to the record (single hop to record). There is just no way for the client to say 'give me a read replica from another node'. The replicas are there for fault-tolerance, if one of the nodes containing the master copy of the record dies, replicas will be used.
I am researching the same problem in the context of hot keys. Every record gets assigned to a node in Dynamo. So a million reads on the same record will lead to hot keys, loss of reads/writes etc. How to deal with this ? A read-replica will work great because I can now manage the hot keys at the application and move all extra reads to read-replica(s). This is again fraught with issues.
Under 3 nodes using redshift we plan on doing 50-100 inserts every 10 seconds. Within that 10 second window we also will try to do the equivalent of a redshift upsert as documented here https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-upsert.html on about 50 to 100 rows as well.
I'm basically unaware if a 10 second window is realistic or a 10 minute window... etc is good for this kind of load. Should this be a daily batch? Should I try to re-architect to get rid of upserts?
My question is essentially can redshift handle this load? I feel the upsert is happening too many times. We are using structured streaming in spark to handle all of this. If yes what type of nodes should we be using? Has anyone who has done this have a ballpark estimate? If no, what are alternative architectures?
Essentially what we're trying to do is load entity data to be joined with the events in redshift. But we want the analytics to be as near real time as possible so we want load as fast as we can.
There's probably no exact answer for this, so any explanation that can get help me perform estimations on requirements based on load will be helpful.
I do not think you will achieve the performance you seek.
Running large numbers of INSERT statements is not an optimal way to load data into Amazon Redshift.
The best way is via running COPY from data stored in Amazon S3. This loads data in parallel across all nodes.
Unless you have a very real need to get data immediately into Redshift, it would be better to batch the data in S3 over a period of time (the larger the batch, the better), then load via COPY. This will also work well with the Staging Table approach to performing UPSERTS.
The best way to discover whether Redshift will handle a particular load is to try it! Spin up another cluster and try the various methods, measuring the performance each time.
I would recommend using Kinesis Firehose to insert data to Redshift. It will optimize for time / load and insert accordingly.
We tried inserting manually in batches, does not seems to be the cleaner way of handling it when an optimized cloud service exist for the same.
https://docs.aws.amazon.com/ses/latest/DeveloperGuide/event-publishing-redshift-firehose-stream.html
It does collect them in batches, compress and load them to Redshift.
Upsert Process:
If you want an upsert this is how I would have them done in a scalable way,
DynamoDB Table (Update) --> DynamoDB Streams --> Lambda --> Firehose --> Redshift
Have a scheduled job to cleanup any duplicate records based on created_timestamp.
Hope it helps.
I am working on Amazon DynamoDB audit table.
The read/write mode was set to "Provisioning". Now, the mode is changed to "On-Demand". I have an "Audit Table" (which captures the audit information like date and time of operation, user details, etc) associated with DynamoDB.
My questions on this are:
1) How is it impacting the data that gets created in the "Audit Table"?
2) Will the data be deleted automatically on timely bases?
3) If not, what is the maximum limit of data that a table (audit table in this case) can persist?
Please let me know if you need any more information from my side.
Waiting for your answers on my questions.
Thanks and regards,
Mahesh Bongale
Provisioning just means that the table is initializing with whatever read/write capacity you set, or OnDemand capacity if you set it to that mode (similar to an auto-scaling mode where it will always deliver the throughput needed by your application). More info: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadWriteCapacityMode.html
No, absolutely not, unless you specifically add code that will delete old data OR set a specific TTL on your data. More info: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/TTL.html
There is no specific limit on the number of rows in a given table. It can be as much as you want. There are a few limits though on a few things, some can be lifted if you ask AWS, some can not: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Limits.html