I am new to scalding world. My scalding job will have multiple stages, and I need to tune each stage individually.
I have found that we might be able to change the number of reducers by using withReducers. Also, I am able to set the split size for the input data by the job config. However, I didn't see there is any way to change the number of mappers for my sub-tasks on the fly.
Did I miss something? Does anyone know how to specify the number of mappers for my sub-tasks? Thanks.
Got some answers/ideas might be helpful for someone else who shared the same question.
It is much easier to control reducers compared to mappers.
Mappers are controlled by hadoop without a similar simple knob. You can set some config parameters to give hadoop an idea of how many map tasks to launch.
This stack overflow may be helpful:
Setting the number of map tasks and reduce tasks
One workaround I could think of is changing your major task to small ones, which you could individually tweak the size (# of mappers) of your input data.
I am facing below warning for cache creation in session logs for sorter transformation in Informatica PowerCenter
**Warning in session log.*
Sorter Transformation [HIGHYIELDSPRDDELTA] required 2-pass sort (1-pass temp I/O: 23224320 bytes). You may try to set the cache size
to 30 MB or higher for 1-pass in-memory sort.
On the web, people suggest to calculate the size of data in the file and hard code it in properties of sorter transformation but that is not feasible in our project and this approach will not be a dynamic approach if data size increases the mapping will give an error.
I want a dynamic solution to tune the performance of the mapping, in short, I want to reduce the time of cache file creation. please suggest any other method to tune the mapping
Any help will appreciate.
This warning means : all given data can't be sort at once as given cache/memory size is not enough. Data will be sorted in segments and combine later accordingly. You may face some performance issue some time. The best approach could be :
Read the sorted data if you are using any rational DB system provided your business logic not impacted.
Increase the cache memory size (rather than auto) to process max possible data.
hope this helps.
It sounds like you haven't set the sorter cache size to Auto. Please check and amend.
Have a look at following link
https://kb.informatica.com/h2l/HowTo%2520Library/1/0794-OptimizingSessionCachesInPowerCenter-H2L.pdf&ved=0ahUKEwio7dHIz4TWAhVGFMAKHVW_BOIQFgglMAA&usg=AFQjCNH7O6r7Tmdps2bgQll5qAjga-FgvQ
i.e. the two settings on the config tab dynamically control how much memory is shared between all transformations set to auto cache size
Maximum memory allowed for Auto Cache attributes
Maximum Percentage of Total Memory Allowed for Auto Memory Attributes
I am working on a map/reduce review and I always have reduce_overflow_error each time I run the view, if I set reduce_limit = false in couchdb configuration, it is working, I want to know if there is negative effect if I change this config setting? thank you
The setting reduce_limit=true enforces CouchDB to control the size of reduced output on each step of reduction. If stringified JSON output of a reduction step has more than 200 chars and it‘s twice or more longer than input, CouchDB‘s query server throws an error. Both numbers, 2x and 200 chars, are hard-coded.
Since a reduce function runs inside SpiderMonkey instance(s) with only 64Mb RAM available, the limitation set by default looks somehow reasonable. Theoretically, reduce must fold, not blow up the data given.
However, in real life it‘s quite hard to fly under the limitation in all cases. You can not control number of chunks for a (re)reduction step. It means you can run into situation, when your output for a particular chunk is more than twice longer in chars, although other chunks reduced are much shorter. In this case even one uncomfortable chunk breaks entire reduction if reduce_limit is set.
So unsetting reduce_limit might be helpful, if your reducer can sometimes output more data, than it received.
Common case – unrolling arrays into objects. Imagine you receive list of arrays like [[1,2,3...70], [5,6,7...], ...] as input rows. You want to aggregate your list in a manner {key0:(sum of 0th elts), key1:(sum of 1st elts)...}.
If CouchDB decides to send you a chunk with 1 or 2 rows, you have an error. Reason is simple – object keys are also accounted calculating result length.
Possible (but very hard to achieve) negative effect is SpiderMonkey instance constantly restarting/falling on RAM overquota, when trying to process a reduction step or entire reduction. Restarting SM is CPU and RAM intensive and costs hundreds milliseconds in general.
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.