But the big issue with databases I've worked with is not how many inserts you do per second, even spinning rust, if properly reasoned can do -serious- inserts per second in append only data structures like myisam, redis even lucene. However the issue comes when you want to read that data or, more horribly, update that data. Updates, by definition are a read and a write to commuted data, this can cause fragmentation and other huge headaches.
MySQL Cluster (NDB - not Innodb, not MyISM. NDB is the network database engine - built by Ericsson, taken on by MySQL. It is a distributed, in-memory, no shared state DB).
>50% of mobile calls use NDB as a Home Location Registry, and they can handle >1m transactional writes per second. We have verified the benchmarks, that you can get millions of transactions/sec (updates are about 50% slower than reads) on commodity hardware with just gigabit ethernet.
Oracle claim up to 200m transactions/sec on Infiniband and good hardware:
http://www.slideshare.net/frazerClement/200-million-qps-on-c...
Two amazing facts about MySQL Cluster:
(1) It's open source
(2) It's never penetrated the Silicon Valley Echo Chamber, but is still the world's best DB for write-intensive transactional workloads.
Another reason may be that it seems you need to be an expert in internal workings of the ndb to deal with it. I tried to use it. Over a month ended up with a few cases of "can't start the database", "can't update", etc. with no real solutions. There's not that much information about ndb on the internet, so googling doesn't help. The best help I got was from mysql-ndb irc channel, but in practice when things went bad, people said something like "if you send me the database, I'll help you figure it out". This does not work in practice.
I feel like it would be more popular if people actually wrote about how they're using it. But how do you start when even basic configuration bugs are still open without progress: https://bugs.mysql.com/bug.php?id=28292
(this was a few years ago or so, maybe things changed)
Yes, that's a good few years ago. Stability issues were mostly fixed around 8 years ago. It's now very stable. There are products like www.severalnines.com and MySQL Manager to setup and manage instances for you with a UI.
If you want to roll your own, there are chef cookbooks for installing ndb - https://github.com/hopshadoop/ndb-chef .
Some systems (like Cassandra) has upserts and can do writes without reading. Though you loose any kind of "transaction" safety in that you are not sure what you are writing on top of. But in my experience for the vast majority of cases that is ok.
> Some systems (like Cassandra) has upserts and can do writes without reading.
That forces the latest-version resolution into a read-side problem - systems like that cannot handle any sort of complete table-scans but can only really work for a pre-known key.
With a known unique key, this is somewhat possible to use systems like this, but extremely expensive when what you need is BETWEEN '2017-01-01' and '2017-01-02'.
We have a lot of data like that and just use a compound partition key of (year, month, day) tuple and then we can ask stuff on day resolution. We also add exact timestamp into clustering key so you can only ask for more precise data.
With Cassandra (well most nosql dbs that I've run into) you model your data in the db by the queries. So if you want to scan by date then you create a new table (or a materialized view) with the date as the key you query for. Though even for then you have to keep in mind the amount of data to make sure you don't have too much data per partition and hotspots.
Cassandra has 2 components to its primary key - 1 that sets the partition (which includes which nodes get the data), and 1 that sets the clustering/sorting/ordering within that partition.
If you use a clustering key, scans become trivial within a given partition.
You are so right. Without giving to much detail the wife is working on a project with a branch of government. They use Oracle. Her teams biggest problems are and have been the DB falling over during updates or joins. Were talking billions of rows of government data. Running out of table space, loads taking literally days to complete. Thing's that I have never seen happen. I'm not sure if it's the DB architecture itself or clueless people doing really dumb things. I only get to see these failures second hand.
Not really a database per se, but trading system matching engines can maintain updates in excess of 1M/s. Of course these are small 60 byte messages updating 10-15 bytes of a given order book in something like a RB-tree, so it's not as impressive.
What is impressive I guess is that they can sustain 1M updates/sec at a 99.9th percentile update round-trip of ~10 microseconds or so, with medians at around ~4 microseconds.
Since each ingest process talked directly to the Accumulo tablet local to it, it really measured loopback+RPC+DFS performance. Knowing how these things usually go, it might have been 100M rows/s but only 100k-1M RPCs/s. It's still quite impressive, but it's important to keep it in perspective. For example, I believe Google's C* 1M writes/s demo also included real network overhead from driver processes. Additionally, that was with the WAL on, vs. this Accumulo run which disabled the WAL.
Our graph store (HBase, SSD) on 10 nodes can easily support 3M edges/s read/stored, but thats ~40k RPCs/s given our column sizes and average batch size.
A transaction log needs to be cleaned up, which takes time. You might amortize cleanup over many small objects.
> but it does work for eg some file systems---which are also a type of database
No mainstream FS uses the txn log as a primary store. The log(s) are only around for active writes to metadata, as soon as the second write is out the spot in the log can/will be reused. Similar to the clean up case FSes try to batch as many ops as they fit into their log(s) before flushing the transaction.
Apart from the CoW herd (ZFS, btrfs, HAMMER) most FSes either can't or won't journal data by default anyway. -- Which is a fairly often overlooked point, many people seem to assume that a journaling FS means that everything, include their application data, is journaled, which isn't just wrong, but can also be a rather dangerous assumption; depending on application.
I think this is a piece of the pie. The thing to recognize that I think is just as important is that it is possible to state several common use cases (even synchronous microservices) as collections of append only immutable logs for system of record and an in-memory read/view state for readers and mutating functions.
I am using this pattern in risk, fraud and commerce and once new members in my teams get over the mental barrier of decoupling the append only log from state, it all just clicks for them.
So... When do you reach the point Where it's better to just use a hashtable in ram? Super high speed "in memory" databases are still beat by manipulating the data structures yourself.
I feel like there's very limited applications where all out speed is important and it's better to use a database than just do the operation yourself in ram and save the network overhead.
You can insert billions of items per second into a hashtable, and when you're working in your own app memory transactions aren't needed.
The key point is that they managed to do it with Accumulo; the insertion rate through storage is otherwise unremarkable. For 10 GbE clusters, line-rate insertion has been relatively easy to achieve for several years now.
An important point is that they disabled all of the durability, replication, and safety features. Graph500 records are quite small so that insertion rate given the size of their cluster implies average throughput that is significantly less than line-rate.
I was wondering. Does anyone here use Accumulo internally or for a client?
I had not heard of it before and the line "widely used for government applications" made me wonder why I hadn't. I'm a consultant working with graphs in Norway and this database is completely new to me.
It was developed by the NSA based on Google's Bigtable paper, so it has a lot of US government users. I think Apache HBase or Cassandra are far more popular NoSQL solutions for most users.
This comment branch is a weird topic, but the implication of these definitions is that modern languages come packaged with multiple databases, some of which can scale to billions of writes per second (arrays, lists, sets, etc.)
People used to get filthy rich (hi there Larry) building databases (which has all sorts of data access & management goodies) until 21st century came along and in the name of "progress" a hashtable with an HTTP interface was called a "database". Of course as nod to the said filthy rich guys from the 20th century we called them "noSQL" "databases" so as to let them continue to charge money from all those silly people in the 20th century that built their information systems on (the now "defunct" :) "databases".
> All the in-memory stuff is nothing but a cache, distributed or not.
That's flat out wrong.
A cache is a limited store that maintains a sub-set of the data that has been placed in it. Typically the eviction policy is temporal.
A database or datastore on the other hand requires explicit removal of data that has been placed in it.
p.s. Which is why we can meaningfully talk about "cache misses" in context of a perfectly functional cache, but "missing" data in a database is generally discussed in context of a buggy datastore or database.
p.s.s. Most certainly we can have pure in-memory datastores/databases, as semantics of a database are orthogonal to its datastore component. And you don't have to take some random hn user's word for it. Go ahead and ask Michael Stonebraker. :)
Oh, come on. Old time RDBMS have been designed to survive a sudden power outage - the system would rollback all the partially committed transactions to recover to the consistent state of the database.
The durability and data consistency is what defines a database in the first place.
For cache vs. database metaphor - think of the difference between a RAM-disk and HDD. This is fundamental difference. Protocol details are irrelevant.
This is why, say, sqlite is a database, while redis is not.
Reducing technology assessment to puns is ultimately not very informative. I recommend you read up on Stonebraker's work the past few years. Durability issues of in-mem DBs are addressed.
It is certainly true that many pure mem vendors out there are playing fast and loose with terminology, but if one were to accept your assertions, then when the day arrives when spinning disks are antiques then I guess we no longer would have databases.
Nope. Old definitions still holds. If some hipsters are re-using the word to bullshit people it does not mean that correct connotations are deprecated.
But the big issue with databases I've worked with is not how many inserts you do per second, even spinning rust, if properly reasoned can do -serious- inserts per second in append only data structures like myisam, redis even lucene. However the issue comes when you want to read that data or, more horribly, update that data. Updates, by definition are a read and a write to commuted data, this can cause fragmentation and other huge headaches.
I'd love to see someone do updates 1,000,000/s