Apparently (based on an exception) EventHubClient.SendBatch and EventHubClient.SendBatchAsync only support sending to a single partition per operation. This appears to be indicated indicated in the documentation by the method summary "Sends a batch of event data to the logical partition represented by PartitionId" which appears to be copied from the partition specific EventHubSender.SendBatch.
Are there design considerations (vs just writing less code) in having the higher level client not rebatch as needed?
The EventHubClient has control over the partition key hashing/distribution which is not available to callers of EventHubClient that wish to send a batch of data with differing keys that may lie on the same partition. Left to rebatch myself I need to make calls on the order of the number of messages as opposed to on the number of partitions which with small messages is easily two orders of magnitude difference.
Since it's already necessary to rebatch it could be worse.
I was assuming the PartitionKey on your EventData objects of the batch would be used to partition out. But apparently not.
However, there's Paolo Salvatori, who wrote a nice set of Extension methods to provide good and easy support for sending in batches to Event Hub.
You'll probably like his post here: http://blogs.msdn.com/b/paolos/archive/2015/03/26/how-to-implement-a-partitioned-sendbatch-method-for-azure-service-bus-entities.aspx
Best regards
Related
I have data I have to join at the record level. For example data about users is coming in from different source systems but there is not a common primary key or user identifier
Example Data
Source System 1:
{userid = 123, first_name="John", last_name="Smith", many other columns...}
Source System 2:
{userid = EFCBA-09DA0, fname="J.", lname="Smith", many other columns...}
There are about 100 rules I can use to compare one record to another
to see if customer in source system 1 is the same as source system 2.
Some rules may be able to infer record values and add data to a master record about a customer.
Because some rules may infer/add data to any particular record, the rules must be re-applied again when a record changes.
We have millions of records per day we'd have to unify
Apache Beam / Dataflow implementation
Apache beam DAG is by definition acyclic but I could just republish the data through pubsub to the same DAG to make it a cyclic algorithm.
I could create a PCollection of hashmaps that continuously do a self join against all other elements but this seems it's probably an inefficient method
Immutability of a PCollection is a problem if I want to be constantly modifying things as it goes through the rules. This sounds like it would be more efficient with Flink Gelly or Spark GraphX
Is there any way you may know in dataflow to process such a problem efficiently?
Other thoughts
Prolog: I tried running on subset of this data with a subset of the rules but swi-prolog did not seem scalable, and I could not figure out how I would continuously emit the results to other processes.
JDrools/Jess/Rete: Forward chaining would be perfect for the inference and efficient partial application, but this algorithm is more about applying many many rules to individual records, rather than inferring record information from possibly related records.
Graph database: Something like neo4j or datomic would be nice since joins are at the record level rather than row/column scans, but I don't know if it's possible in beam to do something similar
BigQuery or Spanner: Brute forcing these rules in SQL and doing full table scans per record is really slow. It would be much preferred to keep the graph of all records in memory and compute in-memory. We could also try to concat all columns and run multiple compare and update across all columns
Or maybe there's a more standard way to solving these class of problems.
It is hard to say what solution works best for you from what I can read so far. I would try to split the problem further and try to tackle different aspects separately.
From what I understand, the goal is to combine together the matching records that represent the same thing in different sources:
records come from a number of sources:
it is logically the same data but formatted differently;
there are rules to tell if the records represent the same entity:
collection of rules is static;
So, the logic probably roughly goes like:
read a record;
try to find existing matching records;
if matching record found:
update it with new data;
otherwise save the record for future matching;
repeat;
To me this looks very high level and there's probably no single 'correct' solution at this level of detail.
I would probably try to approach this by first understanding it in more detail (maybe you already do), few thoughts:
what are the properties of the data?
are there patterns? E.g. when one system publishes something, do you expect something else from other systems?
what are the requirements in general?
latency, consistency, availability, etc;
how data is read from the sources?
can all the systems publish the records in batches in files, submit them into PubSub, does your solution need to poll them, etc?
can the data be read in parallel or is it a single stream?
then the main question of how can you efficiently match a record in general will probably look different under different assumptions and requirements as well. For example I would think about:
can you fit all data in memory;
are your rules dynamic. Do they change at all, what happens when they do;
can you split the data into categories that can be stored separately and matched efficiently, e.g. if you know you can try to match some things by id field, some other things by hash of something, etc;
do you need to match against all of historical/existing data?
can you have some quick elimination logic to not do expensive checks?
what is the output of the solution? What are the requirements for the output?
If I scan or query in DynamoDB it is possible to set the Limit property. The DynamoDB documentation says the following:
The maximum number of items to evaluate (not necessarily the number of
matching items).
So the problem with this is if you set filters and such it won't return all the items.
My goal that I'm trying to figure out how to achieve is to have a filter in a scan or query, but have it return x number of items. No matter what. I'm ok with having to use LastEvaluatedKey and make multiple requests, but I would like to try to make it as seamless and easy as possible (so not doing that would be best.
The only way I have thought to do this is to set the Limit property to say 1 or something. Then just keep scanning or querying using the LastEvaluatedKey until I reach that x number of items I'm looking for. Problem is, this seems VERY wasteful and inefficient. I mean if you have a table of millions of records you might have to make thousands and thousands of requests. It doesn't seem like it scales very well. Of course I'm sure it's no different than what DynamoDB would be doing behind the scenes.
But is there a way to do this more efficiently where I can reduce the number of requests I have to make? Or is that the only way to achieve this?
How would you achieve this goal?
A single Query operation will read up to the maximum number of items set (if using the Limit parameter) or a maximum of 1 MB of data and then apply any filtering to the results using FilterExpression.
You're 100% right that Limit is applied before FilterExpression. Meaning Dynamo might return some number or documents less than the Limit while other documents that satisfy the FilterExpression still exist in the table but aren't returned.
Its sounds like it would be unacceptable for your api to behave in the same manner. That is going to mean that in some cases, a single request to your service will result in multiple requests to Dynamo. Also, keep in mind that there is no way to predict what the LastEvaluatedKey will be which would be required to parallelize these requests. So in the case that your service makes multiple requests to Dynamo, they will be serial. To me, this is a rather heavy tradeoff but, if it is a requirement that you satisfy the Limit whenever possible, you have options.
First, Dynamo will automatically page at 1 MB. That means you could simply send your query to Dynamo without a Limit and implement the Limit on your end. You may still need to make multiple requests to ensure that your've satisfied the Limit but this approach will result in the fewest number of requests to Dynamo. The trade off here is the total data being read and transferred. Chances are your Limit will not happen to line up perfectly with the 1 MB limit which means the excess data being read, filtered, and transferred is wasted.
You already mentioned the other extreme of sending a Limit of 1 and pointed out that will result in the maximum number of requests to Dynamo
Another approach along these lines is to create some sort of probabilistic function that takes the Limit given to your service by the client and computes a new Limit for Dynamo. For example, your FilterExpression filters out about half of the documents in the table. That means you can multiply the client Limit by 2 and that would be a reasonable Limit to send to Dynamo. Of the approaches we've talked about so far, this one has the highest potential for efficiency however, it also has the highest potential for complexity. For example, you might find that using a simple linear function is not good enough and instead you need to use machine learning to find a multi-variate non-linear function to calculate the new Limit. This approach also heavily depends on the uniformity of your data in Dynamo as well as your access patterns. Again, you might need machine learning to optimize for those variables.
In any of the cases where you are implementing the Limit on your end, if you plan on sending back the LastEvaluatedKey to the client for subsequent calls to your service, you will also need to take care to keep track of the LastEvaluatedKey that you evaluated. You will no longer be able to rely on the LastEvaluatedKey returned from Dynamo.
The final approach would be to reorganize/regroup your data either with a GSI, a separate table that you keep in sync using Dynamo Streams or a different schema altogether with the goal of not requiring a FilterExpression.
In an attempt to use Dynamodb for one of projects, I have a doubt regarding the strong consistency model of dynamodb. From the FAQs
Strongly Consistent Reads — in addition to eventual consistency, Amazon DynamoDB also gives you the
flexibility and control to request a strongly consistent read if your application, or an element of your application, requires it. A strongly consistent read returns a result that reflects all writes that received a successful response prior to the read.
From the definition above, what I get is that a strong consistent read will return the latest write value.
Taking an example: Lets say Client1 issues a write command on Key K1 to update the value from V0 to V1. After few milliseconds Client2 issues a read command for Key K1, then in case of strong consistency V1 will be returned always, however in case of eventual consistency V1 or V0 may be returned. Is my understanding correct?
If it is, What if the write operation returned success but the data is not updated to all replicas and we issue a strongly consistent read, how it will ensure to return the latest write value in this case?
The following link
AWS DynamoDB read after write consistency - how does it work theoretically? tries to explain the architecture behind this, but don't know if this is how it actually works? The next question that comes to my mind after going through this link is: Is DynamoDb based on Single Master, multiple slave architecture, where writes and strong consistent reads are through master replica and normal reads are through others.
Short answer: Writing successfully in strongly consistent mode requires that your write succeed on a majority of servers that can contain the record, therefore any future consistent reads will always see the same data, because a consistent read must read a majority of the servers that can contain the desired record. If you do not perform a strongly consistent read, the system will ask a random server for the record, and it is possible that the data will not be up-to-date.
Imagine three servers. Server 1, server 2 and server 3. To write a strongly consistent record, you pick two servers at minimum, and write the data. Let's pick 1 and 2.
Now you want to read the data consistently. Pick a majority of servers. Let's say we picked 2 and 3.
Server 2 has the new data, and this is what the system returns.
Eventually consistent reads could come from server 1, 2, or 3. This means if server 3 is chosen by random, your new write will not appear yet, until replication occurs.
If a single server fails, your data is still safe, but if two out of three servers fail your new write may be lost until the offline servers are restored.
More explanation:
DynamoDB (assuming it is similar to the database described in the Dynamo paper that Amazon released) uses a ring topology, where data is spread to many servers. Strong consistency is guaranteed because you directly query all relevant servers and get the current data from them. There is no master in the ring, there are no slaves in the ring. A given record will map to a number of identical hosts in the ring, and all of those servers will contain that record. There is no slave that could lag behind, and there is no master that can fail.
Feel free to read any of the many papers on the topic. A similar database called Apache Cassandra is available which also uses ring replication.
http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/decandia07dynamo.pdf
Disclaimer: the following cannot be verified based on the public DynamoDB documentation, but they are probably very close to the truth
Starting from the theory, DynamoDB makes use of quorums, where V is the total number of replica nodes, Vr is the number of replica nodes a read operation asks and Vw is the number of replica nodes where each write is performed. The read quorum (Vr) can be leveraged to make sure the client is getting the latest value, while the write quorum (Vw) can be leveraged to make sure that writes do not create conflicts.
Based on the fact that there are no write conflicts in DynamoDB (since these would have to be reconciliated from the client, thus being exposed in the API), we conclude that DynamoDB is using a Vw that respects the second law (Vw > V/2), probably just V/2+1 to reduce write latency.
Now regarding read quorums, DynamoDB provides 2 different kinds of read. The strongly consistent read uses a read quorum that respects the first law (Vr + Vw > V), probably just V/2 if we assume V/2+1 for writes as before. However, an eventually consistent read can use only a single random replica Vr = 1, thus being much quicker but giving zero guarantee around consistency.
Note: There's a possibility that the write quorum used does not respect the second law (Vw > V/2), but that would mean DynamoDB resolves automatically such conflicts (e.g. by selecting the latest one based on local time) without reconciliation from the client. But, I believe that this is really unlikely to be true, since there is no such reference in the DynamoDB documentation. Even in that case though, the rest reasoning stays the same.
You can find answer to your question here: http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/APISummary.html
When you issue a strongly consistent read request, Amazon DynamoDB returns a response with the most up-to-date data that reflects updates by all prior related write operations to which Amazon DynamoDB returned a successful response.
In your example, if the updateItem request to update the value from v0 to v1 was successful, the subsequent strongly consistent read request will return v1.
Hope this helps.
My current approach:
I have one domain class - Application
Each application in my system is stored in "applications" bucket under APPLICATION_KEY key
Apart from application metadata stored in this bucket, each application has its own bucket called "time_metrics/APPLICATION_KEY" where I store time series in a way:
KEY - timestamp / VALUE - some attributes
My concern is efficiency of queries made over specific time window for given application. Currently to get time series from some specific time window and eventually make some reductions I have to make map/reduce over whole "time_metric/APPLICATION_KEY" bucket, which what I have found is not the recommended use case for Riak Map/Reduce.
My question: what would be the best db structure for this kind of a system and how efficiently query it.
Adding onto #macintux's answer.
Basho has had a few customers that have used riak for time series metrics.
Boundary has a nice tech talk about how they use Riak with their network monitoring software. They rollup data into different chunks of time (1m, 5m, 15m) for analysis.
They also have a series of blog posts about lessons learned while implementing this system.
Kivra also has a good slide deck about how they use timeseries data with riak.
You could roll up your data into some sort of arbitrary time length, then read the range you need by issuing regular K/V gets, and then reconstruct the larger picture / reduce in your application.
If you have spare computing power and you know in advance what keys you need, you certainly can use Riak's MapReduce, but often retrieving the keys and running your processing on the client will be as fast (and won't strain your cluster).
Some general ideas:
Roll up your data into larger blocks
If you're concerned about losing data if your client crashes while buffering it, you can always store the data as it arrives
Similar idea: store the data as it arrives, then retrieve it and roll it up at certain intervals
You can automatically expire data once you're confident it is being reliably stored in larger blocks, using either the Bitcask or Memory backends
Memory backend is quite useful (RAM permitting) for any data that only needs to be stored for a limited period of time
Related: don't be afraid to store multiple copies of your data to make reading/reporting easier later
Multiple chunks of time (5- and 15-minute blocks, for example)
Multiple report formats
Having said all that, if you're doing straight key/value requests (it's ideal to always be able to compute the keys you need, rather than doing indexing or searching), Riak can support very heavy traffic loads, so I wouldn't recommend spending too much time creating alternative storage mechanisms unless you know you're going to face latency problems.
Long story short, I'm rewriting a piece of a system and am looking for a way to store some hit counters in AWS SimpleDB.
For those of you not familiar with SimpleDB, the (main) problem with storing counters is that the cloud propagation delay is often over a second. Our application currently gets ~1,500 hits per second. Not all those hits will map to the same key, but a ballpark figure might be around 5-10 updates to a key every second. This means that if we were to use a traditional update mechanism (read, increment, store), we would end up inadvertently dropping a significant number of hits.
One potential solution is to keep the counters in memcache, and using a cron task to push the data. The big problem with this is that it isn't the "right" way to do it. Memcache shouldn't really be used for persistent storage... after all, it's a caching layer. In addition, then we'll end up with issues when we do the push, making sure we delete the correct elements, and hoping that there is no contention for them as we're deleting them (which is very likely).
Another potential solution is to keep a local SQL database and write the counters there, updating our SimpleDB out-of-band every so many requests or running a cron task to push the data. This solves the syncing problem, as we can include timestamps to easily set boundaries for the SimpleDB pushes. Of course, there are still other issues, and though this might work with a decent amount of hacking, it doesn't seem like the most elegant solution.
Has anyone encountered a similar issue in their experience, or have any novel approaches? Any advice or ideas would be appreciated, even if they're not completely flushed out. I've been thinking about this one for a while, and could use some new perspectives.
The existing SimpleDB API does not lend itself naturally to being a distributed counter. But it certainly can be done.
Working strictly within SimpleDB there are 2 ways to make it work. An easy method that requires something like a cron job to clean up. Or a much more complex technique that cleans as it goes.
The Easy Way
The easy way is to make a different item for each "hit". With a single attribute which is the key. Pump the domain(s) with counts quickly and easily. When you need to fetch the count (presumable much less often) you have to issue a query
SELECT count(*) FROM domain WHERE key='myKey'
Of course this will cause your domain(s) to grow unbounded and the queries will take longer and longer to execute over time. The solution is a summary record where you roll up all the counts collected so far for each key. It's just an item with attributes for the key {summary='myKey'} and a "Last-Updated" timestamp with granularity down to the millisecond. This also requires that you add the "timestamp" attribute to your "hit" items. The summary records don't need to be in the same domain. In fact, depending on your setup, they might best be kept in a separate domain. Either way you can use the key as the itemName and use GetAttributes instead of doing a SELECT.
Now getting the count is a two step process. You have to pull the summary record and also query for 'Timestamp' strictly greater than whatever the 'Last-Updated' time is in your summary record and add the two counts together.
SELECT count(*) FROM domain WHERE key='myKey' AND timestamp > '...'
You will also need a way to update your summary record periodically. You can do this on a schedule (every hour) or dynamically based on some other criteria (for example do it during regular processing whenever the query returns more than one page). Just make sure that when you update your summary record you base it on a time that is far enough in the past that you are past the eventual consistency window. 1 minute is more than safe.
This solution works in the face of concurrent updates because even if many summary records are written at the same time, they are all correct and whichever one wins will still be correct because the count and the 'Last-Updated' attribute will be consistent with each other.
This also works well across multiple domains even if you keep your summary records with the hit records, you can pull the summary records from all your domains simultaneously and then issue your queries to all domains in parallel. The reason to do this is if you need higher throughput for a key than what you can get from one domain.
This works well with caching. If your cache fails you have an authoritative backup.
The time will come where someone wants to go back and edit / remove / add a record that has an old 'Timestamp' value. You will have to update your summary record (for that domain) at that time or your counts will be off until you recompute that summary.
This will give you a count that is in sync with the data currently viewable within the consistency window. This won't give you a count that is accurate up to the millisecond.
The Hard Way
The other way way is to do the normal read - increment - store mechanism but also write a composite value that includes a version number along with your value. Where the version number you use is 1 greater than the version number of the value you are updating.
get(key) returns the attribute value="Ver015 Count089"
Here you retrieve a count of 89 that was stored as version 15. When you do an update you write a value like this:
put(key, value="Ver016 Count090")
The previous value is not removed and you end up with an audit trail of updates that are reminiscent of lamport clocks.
This requires you to do a few extra things.
the ability to identify and resolve conflicts whenever you do a GET
a simple version number isn't going to work you'll want to include a timestamp with resolution down to at least the millisecond and maybe a process ID as well.
in practice you'll want your value to include the current version number and the version number of the value your update is based on to more easily resolve conflicts.
you can't keep an infinite audit trail in one item so you'll need to issue delete's for older values as you go.
What you get with this technique is like a tree of divergent updates. you'll have one value and then all of a sudden multiple updates will occur and you will have a bunch of updates based off the same old value none of which know about each other.
When I say resolve conflicts at GET time I mean that if you read an item and the value looks like this:
11 --- 12
/
10 --- 11
\
11
You have to to be able to figure that the real value is 14. Which you can do if you include for each new value the version of the value(s) you are updating.
It shouldn't be rocket science
If all you want is a simple counter: this is way over-kill. It shouldn't be rocket science to make a simple counter. Which is why SimpleDB may not be the best choice for making simple counters.
That isn't the only way but most of those things will need to be done if you implement an SimpleDB solution in lieu of actually having a lock.
Don't get me wrong, I actually like this method precisely because there is no lock and the bound on the number of processes that can use this counter simultaneously is around 100. (because of the limit on the number of attributes in an item) And you can get beyond 100 with some changes.
Note
But if all these implementation details were hidden from you and you just had to call increment(key), it wouldn't be complex at all. With SimpleDB the client library is the key to making the complex things simple. But currently there are no publicly available libraries that implement this functionality (to my knowledge).
To anyone revisiting this issue, Amazon just added support for Conditional Puts, which makes implementing a counter much easier.
Now, to implement a counter - simply call GetAttributes, increment the count, and then call PutAttributes, with the Expected Value set correctly. If Amazon responds with an error ConditionalCheckFailed, then retry the whole operation.
Note that you can only have one expected value per PutAttributes call. So, if you want to have multiple counters in a single row, then use a version attribute.
pseudo-code:
begin
attributes = SimpleDB.GetAttributes
initial_version = attributes[:version]
attributes[:counter1] += 3
attributes[:counter2] += 7
attributes[:version] += 1
SimpleDB.PutAttributes(attributes, :expected => {:version => initial_version})
rescue ConditionalCheckFailed
retry
end
I see you've accepted an answer already, but this might count as a novel approach.
If you're building a web app then you can use Google's Analytics product to track page impressions (if the page to domain-item mapping fits) and then to use the Analytics API to periodically push that data up into the items themselves.
I haven't thought this through in detail so there may be holes. I'd actually be quite interested in your feedback on this approach given your experience in the area.
Thanks
Scott
For anyone interested in how I ended up dealing with this... (slightly Java-specific)
I ended up using an EhCache on each servlet instance. I used the UUID as a key, and a Java AtomicInteger as the value. Periodically a thread iterates through the cache and pushes rows to a simpledb temp stats domain, as well as writing a row with the key to an invalidation domain (which fails silently if the key already exists). The thread also decrements the counter with the previous value, ensuring that we don't miss any hits while it was updating. A separate thread pings the simpledb invalidation domain, and rolls up the stats in the temporary domains (there are multiple rows to each key, since we're using ec2 instances), pushing it to the actual stats domain.
I've done a little load testing, and it seems to scale well. Locally I was able to handle about 500 hits/second before the load tester broke (not the servlets - hah), so if anything I think running on ec2 should only improve performance.
Answer to feynmansbastard:
If you want to store huge amount of events i suggest you to use distributed commit log systems such as kafka or aws kinesis. They allow to consume stream of events cheap and simple (kinesis's pricing is 25$ per month for 1K events per seconds) – you just need to implement consumer (using any language), which bulk reads all events from previous checkpoint, aggregates counters in memory then flushes data into permanent storage (dynamodb or mysql) and commit checkpoint.
Events can be logged simply using nginx log and transfered to kafka/kinesis using fluentd. This is very cheap, performant and simple solution.
Also had similiar needs/challenges.
I looked at using google analytics and count.ly. the latter seemed too expensive to be worth it (plus they have a somewhat confusion definition of sessions). GA i would have loved to use, but I spent two days using their libraries and some 3rd party ones (gadotnet and one other from maybe codeproject). unfortunately I could only ever see counters post in GA realtime section, never in the normal dashboards even when the api reported success. we were probably doing something wrong but we exceeded our time budget for ga.
We already had an existing simpledb counter that updated using conditional updates as mentioned by previous commentor. This works well, but suffers when there is contention and conccurency where counts are missed (for example, our most updated counter lost several million counts over a period of 3 months, versus a backup system).
We implemented a newer solution which is somewhat similiar to the answer for this question, except much simpler.
We just sharded/partitioned the counters. When you create a counter you specify the # of shards which is a function of how many simulatenous updates you expect. this creates a number of sub counters, each which has the shard count started with it as an attribute :
COUNTER (w/5shards) creates :
shard0 { numshards = 5 } (informational only)
shard1 { count = 0, numshards = 5, timestamp = 0 }
shard2 { count = 0, numshards = 5, timestamp = 0 }
shard3 { count = 0, numshards = 5, timestamp = 0 }
shard4 { count = 0, numshards = 5, timestamp = 0 }
shard5 { count = 0, numshards = 5, timestamp = 0 }
Sharded Writes
Knowing the shard count, just randomly pick a shard and try to write to it conditionally. If it fails because of contention, choose another shard and retry.
If you don't know the shard count, get it from the root shard which is present regardless of how many shards exist. Because it supports multiple writes per counter, it lessens the contention issue to whatever your needs are.
Sharded Reads
if you know the shard count, read every shard and sum them.
If you don't know the shard count, get it from the root shard and then read all and sum.
Because of slow update propogation, you can still miss counts in reading but they should get picked up later. This is sufficient for our needs, although if you wanted more control over this you could ensure that- when reading- the last timestamp was as you expect and retry.