Each index batch is limited from 1 to 1000 documents. When I call it from my local machine or azure VM, I got 800ms to 3000ms per 1000 doc batch. If I submit multiple batches with async, the time spent is roughly the same. That means it would take 15 - 20 hours for my ~50M document collection.
Is there a way I can make it faster?
It looks like you are using our Standard S1 search service and although there are a lot of things that can impact how fast data can be ingested. I would expect to see ingestion to a single partition search service at a rate of about 700 docs / second for an average index, so I think your numbers are not far off from what I would expect, although please note that these are purely rough estimates and you may see different results based on any number of factors (such as number of fields, quantity of facets, etc)..
It is possible that some of the extra time you are seeing is due to the latency of uploading the content from your local machine to Azure, and it would likely be faster if you did this directly from Azure but if this is just a one time-upload that probably is not worth the effort.
You can slightly increase the speed of data ingestion by increasing the number of partitions you have and the S2 Search Service will also ingest data faster. Although both of these come at a cost.
By the way, if you have 50 M documents, please make sure that you allocate enough partitions since a single S1 partition can handle 15M documents or 25GB so you will definitely need extra partitions for this service.
Also as another side note, when you are uploading your content (and especially if you choose to do parallelized uploads), keep an eye on the HTTP responses because if the search service exceeds the resources available you could get HTTP 207 (indicating one or more item failed to apply) or 503's indicating the whole batch failed due to throttling. If throttling occurs, you would want to back off a bit to let the service catch up.
I think you're reaching the request capacity:
https://azure.microsoft.com/en-us/documentation/articles/search-limits-quotas-capacity/
I would try another tier (s1, s2). If you still face the same problem, try get in touch with support team.
Another option:
Instead of pushing data, try to add your data to the blob storage, documentDb or Sql Database, and then, use the pull approach:
https://azure.microsoft.com/en-us/documentation/articles/search-howto-indexing-azure-blob-storage/
Related
I'm a beginner to Azure. I'm using log monitors to view the logs for a Cosmos DB resource. I could see one log with Replace operation which is consuming a lot of average RUs.
Generally operation names should be CREATE/DELETE/UPDATE/READ . But why REPLACE operation has come in place over here - I could not understand it. And why the REPLACE operation is consuming lot of RUs?
What can I try next?
Updates in Cosmos are full replacement operations rather than in-place updates, as such these consume more RU/s than inserts. Also, the larger the document, the more throughput required for the update.
Strategies to optimize throughput consumption on update operations typically center around splitting documents into two with properties that don't change going into one document that is typically larger and another document with those properties that change frequently going into an other that is smaller. This will allow for the updates to be made on a smaller document which reduces RU/s consumed to do the operation.
All that said, 12 RU/s is not an inordinate amount of RU/s for a replace operation. I don't think you will get much, if any throughput reduction doing this. But you can certainly try.
We migrated our mobile app (still being developed) from Parse to Azure. Everything is running, but the price of DocumentDB is so high that we can't continue with Azure without fix that. Probably we're doing something wrong.
1) Price seams to have a bottleneck in the DocumentDB requests.
Running a process to load the data (about 0.5 million documents), memory and CPU was ok, but the DocumentDB request limit was a bottleneck, and the price charged was very high.
2) Even after the end of this data migration (few days of processing), azure continue to charge us every day.
We can't understand what is going on here. The graphic for use are flat, but the price is still climbing, as you can see in the imagens.
Any ideas?
Thanks!
From your screenshots, you have 15 collections under the Parse database. With Parse: Aside from the system classes, each of your user-defined classes gets stored in its own collection. And given that each (non-partitioned) collection has a starting run-rate of ~$24/month (for an S1 collection), you can see where the baseline cost would be for 15 collections (around $360).
You're paying for reserved storage and RU capacity. Regardless of RU utilization, you pay whatever the cost is for that capacity (e.g. S2 runs around $50/month / collection, even if you don't execute a single query). Similar to spinning up a VM of a certain CPU capacity and then running nothing on it.
The default throughput setting for the parse collections is set to 1000 RUPS. This will cost $60 per collection (at the rate of $6 per 100 RUPS). Once you finish the parse migration, the throughput can be lowered if you believe the workload decreased. This will reduce the charge.
To learn how to do this, take a look at https://azure.microsoft.com/en-us/documentation/articles/documentdb-performance-levels/ (Changing the throughput of a Collection).
The key thing to note is that DocumentDB delivers predictable performance by reserving resources to satisfy your application's throughput needs. Because application load and access patterns change over time, DocumentDB allows you to easily increase or decrease the amount of reserved throughput available to your application.
Azure is a "pay-for-what-you-use" model, especially around resources like DocumentDB and SQL Database where you pay for the level of performance required along with required storage space. So if your requirements are that all queries/transactions have sub-second response times, you may pay more to get that performance guarantee (ignoring optimizations, etc.)
One thing I would seriously look into is the DocumentDB Cost Estimation tool; this allows you to get estimates of throughput costs based upon transaction types based on sample JSON documents you provide:
So in this example, I have an 8KB JSON document, where I expect to store 500K of them (to get an approx. storage cost) and specifying I need throughput to create 100 documents/sec, read 10/sec, and update 100/sec (I used the same document as an example of what the update will look like).
NOTE this needs to be done PER DOCUMENT -- if you're storing documents that do not necessarily conform to a given "schema" or structure in the same collection, then you'll need to repeat this process for EVERY type of document.
Based on this information, I cause use those values as inputs into the pricing calculator. This tells me that I can estimate about $450/mo for DocumentDB services alone (if this was my anticipated usage pattern).
There are additional ways you can optimize the Request Units (RUs -- metric used to measure the cost of the given request/transaction -- and what you're getting billed for): optimizing index strategies, optimizing queries, etc. Review the documentation on Request Units for more details.
I use Azure Table storage as a time series database. The database is constantly extended with more rows, (approximately 20 rows per second for each partition). Every day I create new partitions for the day's data so that all partition have a similar size and never get too big.
Until now everything worked flawlessly, when I wanted to retrieve data from a specific partition it would never take more than 2.5 secs for 1000 values and on average it would take 1 sec.
When I tried to query all the data of a partition though things got really really slow, towards the middle of the procedure each query would take 30-40 sec for 1000 values.
So I cancelled the procedure just to re start it for a smaller range. But now all queries take too long. From the beginning all queries need 15-30 secs. Can that mean that data got rearranged in a non efficient way and that's why I am seeing this dramatic decrease in performance? If yes is there a way to handle such a rearrangement?
I would definitely recommend you to go over the links Jason pointed above. You have not given too much detail about how you generate your partition keys but from sounds of it you are falling into several anti patterns. Including by applying Append (or Prepend) and too many entities in a single partition. I would recommend you to reduce your partition size and also put either a hash or a random prefix to your partition keys so they are not in lexicographical order.
Azure storage follows a range partitioning scheme in the background, so even if the partition keys you picked up are unique, if they are sequential they will fall into the same range and potentially be served by a single partition server, which would hamper the ability of azure storage service overall to load balance and scale out your storage requests.
The other aspect you should think is how you are reading the entities back, the best recommendation is point query with partition key and row key, worst is a full table scan with no PK and RK, there in the middle you have partition scan which in your case will also be pretty bad performance due to your partition size.
One of the challenges with time series data is that you can end up writing all your data to a single partition which prevents Table Storage from allocating additional resources to help you scale. Similarly for read operations you are constrained by potentially having all your data in a single partition which means you are limited to 2000 entities / second - whereas if you spread your data across multiple partitions you can parallelize the query and yield far greater scale.
Do you have Storage Analytics enabled? I would be interested to know if you are getting throttled at all or what other potential issues might be going on. Take a look at the Storage Monitoring, Diagnosing and Troubleshooting guide for more information.
If you still can't find the information you want please email AzTableFeedback#microsoft.com and we would be happy to follow up with you.
The Azure Storage Table Design Guide talks about general scalability guidance as well as patterns / anti-patterns (see the append only anti-pattern for a good overview) which is worth looking at.
I'm running performance tests against ATS and its behaving a bit weird when using multiple virtual machines against the same table / storage account.
The entire pipeline is non blocking (await/async) and using TPL for concurrent and parallel execution.
First of all its very strange that with this setup i'm only getting about 1200 insertions. This is running on a L VM box, that is 4 cores + 800mbps.
I'm inserting 100.000 rows with unique PK and unique RK, that should leverage the ultimate distribution.
Even more deterministic behavior is the following.
When I run 1 VM i get about 1200 insertions per second.
When I run 3 VM i get about 730 on each insertions per second.
Its quite humors to read the blog post where they are specifying their targets.
https://azure.microsoft.com/en-gb/blog/windows-azures-flat-network-storage-and-2012-scalability-targets/
Single Table Partition– a table partition are all of the entities in a table with the same partition key value, and usually tables have many partitions. The throughput target for a single table partition is:
Up to 2,000 entities per second
Note, this is for a single partition, and not a single table. Therefore, a table with good partitioning, can process up to the 20,000 entities/second, which is the overall account target described above.
What shall I do to be able to utilize the 20k per second, and how would it be possible to execute more than 1,2k per VM?
--
Update:
I've now also tried using 3 storage accounts for each individual node and is still getting the performance / throttling behavior. Which i can't find a logical reason for.
--
Update 2:
I've optimized the code further and now i'm possible to execute about 1550.
--
Update 3:
I've now also tried in US West. The performance is worse there. About 33% lower.
--
Update 4:
I tried executing the code from a XL machine. Which is 8 cores instead of 4 and the double amount of memory and bandwidth and got a 2% increase in performance so clearly this problem is not on my side..
A few comments:
You mention that you are using unique PK/RK to get ultimate
distribution, but you have to keep in mind that the PK balancing is
not immediate. When you first create a table, the entire table will
be served by 1 partition server. So if you are doing inserts across
several different PKs, they will still be going to one partition
server and be bottlenecked by the scalability target for a single
partition. The partition master will only start splitting your
partitions among multiple partition servers after it has identified hot
partition servers. In your <2 minute test you will not see the
benefit of multiple partiton servers or PKs. The throughput in the
article is targeted towards a well distributed PK scheme with
frequently accessed data, causing the data to be divided amongst
multiple partition servers.
The size of your VM is not the issue as
you are not blocked on CPU, Memory, or Bandwidth. You can achieve
full storage performance from a small VM size.
Check out
http://research.microsoft.com/en-us/downloads/5c8189b9-53aa-4d6a-a086-013d927e15a7/default.aspx.
I just now did a quick test using that tool from a WebRole VM in the
same datacenter as my storage account and I acheived, from a single
instance of the tool on a single VM, ~2800 items per second upload
and ~7300 items per second download. This is using 1024 byte
entities, 10 threads, and 100 batch size. I don't know how efficient this tool is or if it disables Nagles Algorithm as I was unable to get great results (I got ~1000/second) using a batch size of 1, but at least with the 100 batch size it shows that you can achieve high items/second. This was done in US West.
Are you using Storage client library 1.7 (Microsoft.Azure.StorageClient.dll) or 2.0 (Microsoft.Azure.Storage.dll)? The 2.0 library has some performance improvements and should yield better results.
I suspect this may have to do with TCP Nagle.
See this MSDN article and this blog post.
In essence, TCP Nagle is a protocol-level optimization that batches up small requests. Since you are sending lots of small requests this is likely to negatively affect your performance.
You can disable TCP Nagle by executing this code when starting your application
ServicePointManager.UseNagleAlgorithm = false;
Are the compute instances and storage account in the same affinity group? Affinity groups ensure that network proximity between the services is optimal and should result in lower latency at the network level.
You can find affinity group configuration under the network tab.
I would tend to believe that the maximum throughput is for an optimized load. For example, I bet you that you can achieve higher performance using Batch requests than individual requests you are doing now. And of course, if you use GUIDs for your PK, you can't Batch in your current test.
So what if you changed your test to batch insert entities in groups of 100 (maximum per batch), still using GUIDs, but for which 100 entities would have the same PK?
Microsoft changed the architecture of the Azure Storage to use eg. SSD's for journaling and 10 Gbps network (instead of standard Harddrives and 1G ps network). Se http://blogs.msdn.com/b/windowsazure/archive/2012/11/02/windows-azure-s-flat-network-storage-and-2012-scalability-targets.aspx
Here you can read that the storage is designed for "Up to 20,000 entities/messages/blobs per second".
My concern is that 20.000 entities (or rows in Table Storage) is actually not a lot.
We have a rather small solution with a table with 1.000.000.000 rows. With only 20.000 entities pr. second it will take more than half a day to read all rows.
I realy hope that the 20.000 entities actually means that you can do up to 20.000 requests pr. second.
I'm pretty sure the 1st generation allowed up to 5.000 requests pr. second.
So my question is. Are there any scenarios where the 1st generation Azure storage is actually more scalable than the second generation?
Any there any other reason we should not upgrade (move our data to a new storage)? Eg. we tried to get ~100 rows pr. partition, because that was what gave us the best performance characteristic. Are there different characteristic for the 2nd generation? Or has there been any changes that might introduce bugs if we change?
You have to read more carefully. The exact quote from the mentioned post is:
Transactions – Up to 20,000 entities/messages/blobs per second
Which is 20k transactions per second. Which is you correctly do hope for. I surely do not expect to have 20k 1M files uploaded to the blob storage. But I do expect to be able to execute 20k REST Calls.
As for tables and table entities, you could combine them in batches. Given the volume you have I expect that you already are using batches. There single Entity Group Transaction is counted as a single transaction, but may contain more than one entity. Now, rather then assessing whether it is low or high figure, you really need a good setup and bandwidth to utilize these 20k transactions per second.
Also, the first generation scalability target was around that 5k requests/sec you mention. I don't see a configuration/scenario where Gen 1 would be more scalable than Gen 2 storage.
Are there different characteristic for the 2nd generation?
The differences are outlined in that blog post you refer.
As for your last concern:
Or has there been any changes that might introduce bugs if we change?
Be sure there are not such changes. Azure Storage service behavior is defined in the REST API Reference. The API is not any different based on Storage service Generation. It is versioned based on features.