I'm using Cassandra for my project and I was facing a timeout issue during writes, the same the guy was receiving in this post Cassandra cluster with bad insert performance and insert stability (at the moment I'm testing with only one node, Java Driver, last release of Cassandra). The application has to insert a huge quantity of data per user once per day (during nights). I have a rest controller that accepts files and then processes them as they arrive in parallel to insert values in Cassandra. I have to insert 1million entries per user, where an entry has up to 8 values (time is not so important, it can take also 10minutes). Following the answer provided in Cassandra cluster with bad insert performance and insert stability I decided to add executeAsync(), Semaphore and PreparedStatement to my application, while previously I was using none of them.
The problem now is that, using variable keyspaces (one per user) and having the necessity to update lists in the database, I can't initialize my PreparedStatements in the initialization phase but I have to do it at least once per file processed (one file contains 10+k entries) and an user has to upload up to 100 files per day. For this reason, I'm getting this warning:
Re-preparing already prepared query INSERT INTO c2bdd9f7073dce28ed973238ac85b6e5d6162fce.sensorMonitoringLog (timestamp, sensorId, isLogging) VALUES (?, ?, ?). Please note that preparing the same query more than once is generally an anti-pattern and will likely affect performance. Consider preparing the statement only once.
My question is: is it a good practice to use PreparedStatement like this or it is better to use normal insert with executeAsync()?
Thank you
If you are facing a timeout issue during write, it is a good idea to use PreparedStatement but not to use asynchronous insert. Timeouts are a way to prevent Cassandra from work overload. With asynchronism you are giving it more work at the same time and the risk of OOM would grow.
To do things properly with PreparedStatement, you have to create one and only one Session object by keyspace. Then each session must prepare its own statement once.
Moreover, be aware their is a thread safety risk with PreparedStatement and asynchronism. Preparing a statement must be synchronized. But once again, I advice you not to use ExecuteAsynch in such case.
Related
I'm running OS Cassandra 3.11.9 with Datastax Java Driver 3.8.0. I have a Cassandra keyspace that has multiple tables functioning as lookup tables / search indices. Whenever I receive a new POST request to my endpoint, I parse the object and insert it in the corresponding Cassandra table. I also put inserts to each corresponding lookup table. (10-20 per object)
When ingesting a lot of data into the system, I've been running into WriteTimeoutExceptions in the driver.
I tried to serialize the insert requests into the lookup tables by introducing Apache Camel and putting all the Statements into a queue that the Session could work off of, but it did not help.
With Camel, since the exceptions are now happening in the Camel thread, the test continues to run, instead of failing on the first exception. Eventually, the test seems to crash Cassandra. (Nothing in the Cassandra logs though)
I also tried to turn off my lookup tables and instead insert into the main table 15x per object (to simulate a similar number of writes as if I had the lookup tables on). This test passed with no exception, which makes me think the large number of tables is the problem.
Is a large number (2k+) of Cassandra tables a code smell? Should we rearchitect or just throw more resources at it? Nothing indicative has shown in the logs, mostly just some status about the number of tables etc - no exceptions)
Can the Datastax Java Driver be used multithreaded like this? It says it is threadsafe.
There is a direct effect of the high number of tables onto the performance - see this doc (the whole series is good source of information), and this blog post for more details. Basically, with ~1000 tables, you get ~20-25% degradation of performance.
That's could be a reason, not completely direct, but related. For each table, Cassandra needs to allocate memory, have a part for it in the memtable, keep information about it, etc. This specific problem could come from the blocked memtable flushes, or something like. Check the nodetool tpstats and nodetool tablestats for blocked or pending memtable flushes. It's better to setup some continuous monitoring solution, such as, metrics collector for Apache Cassandra, and and for period of time watch for the important metrics that include that information as well.
I'm using Cassandra Java driver with a fetch size set to 1k. I need to query all records in a table and perform some time consuming action for a every row.
What will happen if I'll keep the ResultSet open (not fully iterated) for a one day?
What I don't care about:
consistency. If some new record will be written in the meantime, I'm ok to fetch it. However, I'm fine if I won't get it
fault tolerance. If during that process some node will fail, I'm fine if the query will fail too. However, I would like to detect that from the client perspective.
What I care about:
Cassandra resource utilization - I don't want to cause cluster outage due to some blocked resources
lateness - I don't want to block (or slow down much) cluster for other consumers of that table
I would like to get all records which existed when I started the query (assuming no deletions). However, they don't have to be up to date
The paging state is the information about the last read data (literally serialized partition key, clustering, and remaining). When sent to coordinator it will look for everything greater than that. So there are no resources in the server spent for this and no performance impact vs a normal read.
Cassandra does not have any features to allow isolation even within a single query. If data has changed from when the first query was made and the second, you will get the up to date information.
I'm current using DB2 and planning to use cassandra because as i know cassandra have a read performance greater than RDBMS.
May be this is a stupid question but I have experiment that compare read performance between DB2 and Cassandra.
Testing with 5 million records and same table schema.
With query SELECT * FROM customer. DB2 using 25-30s and Cassandra using 40-50s.
But query with where condition SELECT * FROM customer WHERE cusId IN (100,200,300,400,500) DB2 using 2-3s and Cassandra using 3-5ms.
Why Cassandra faster than DB2 with where condition? So i can't prove which database is greater with SELECT * FROM customer right?
FYI.
Cassandra: RF=3 and CL=1 with 3 nodes each node run on 3 computers (VM-Ubuntu)
DB2: Run on windows
Table schema:
cusId int PRIMARY KEY, cusName varchar
If you look at the types of problems that Cassandra is good at solving, then the reasons behind why unbound ("Select All") queries suck become quite apparent.
Cassandra was designed to be a distributed data base. In many Cassandra storage patterns, the number of nodes is greater than the replication factor (I.E., not all nodes contain all of the data). Therefore, limiting the number of network hops becomes essential to modeling high-performing queries. Cassandra performs very well with specific queries (which utilize the partition/clustering key structure), because it can quickly locate the node primarily responsible for the data.
Unbound queries (A.K.A. multi-key queries) incur the extra network time because a coordinator node is required. So one node acts as the coordinator, queries all other nodes, collates data, and returns the result set. Specifying a WHERE clause (with at least a partition key) and while using a "Token Aware" load balancing policy, performs well for two reasons:
A coordinator node is not required.
The node primarily responsible for the range is queried, returning the result set in a single netowrk hop.
tl;dr;
Querying Cassandra with an unbound query, causes it to incur a lot of extra processing and network time that it normally wouldn't have to do, had the query been specified with a WHERE clause.
Even as a troublesome query like a no-condition range query, 40-50s is pretty extreme for C*. Is the coordinator hitting GCs with the coordination? Can you include code used for your test?
When you make a select * vs millions of records, it wont fetch them all at once, it will grab the fetchSize at a time. If your just iterating through this, the iterator will actually block even if you used executeAsync initially. This means that every 10k (default) records it will issue a new query that you will block on. The serialized nature of this will take time just from a network perspective. http://docs.datastax.com/en/developer/java-driver/3.1/manual/async/#async-paging explains how to do it in a non-blocking way. You can use this to to kick off the next page fetch while processing the current which would help.
Decreasing the limit or fetch size could also help, since the coordinator may walk token ranges (parallelism is possible here but its heuristic is not perfect) one at a time until it has read enough. If it has to walk too many nodes to respond it will be slow, this is why empty tables can be very slow to do a select * on, it may serially walk every replica set. With 256 vnodes this can be very bad.
This question is about NoSQL (for instance take cassandra).
Is it true that when you use a NoSQL database without data replication that you have no consistency concerns? Also not in the case of access concurrency?
What happens in case of a partition where the same row has been written in both partitions, possible multiple times? When the partition is gone, which written value is used?
Let's say you use N=5 W=3 R=3. This means you have guaranteed consistency right? How good is it to use this quorum? Having 3 nodes returning the data isn't that a big overhead?
Can you specify on a per query basis in cassandra whether you want the query to have guaranteed consistency? For instance you do an insert query and you want to enforce that all replica's complete the insert before the value is returned by a read operation?
If you have: employees{PK:employeeID, departmentId, employeeName, birthday} and department{PK:departmentID, departmentName} and you want to get the birthday of all employees with a specific department name. Two problems:
you can't ask for all the employees with a given birthday (because you can only query on the primary key)
You can't join the employee and the department column families because joins are impossible.
So what you can do is create a column family:
departmentBirthdays{PK:(departmentName, birthday), [employees-whos-birthday-it-is]}
In that case whenever an employee is fired/hired it has to be removed/added in the departmentBirthdays column family. Is this process something you have to do manually? So you have to manually create queries to update all redundant/denormalized data?
I'll answer this from the perspective of cassandra, coz that's what you seem to be looking at (hardly any two nosql stores are the same!).
For a single node, all operations are in sequence. Concurrency issues can be orthogonal though...your web client may have made a request, and then another, but due to network load, cassandra got the second one first. That may or may not be an issue. There are approaches around such problems, like immutable data. You can also leverage "lightweight transactions".
Cassandra uses last write wins to resolve conflicts. Based on you replication factor and consistency level for your query, this can work well.
Quurom for reads AND writes will give you consistency. There is an edge case..if the coordinator doesn't know a quorum node is down, it sends the write requests, then the write would complete when quorum is re-established. The client in this case would get a timeout and not a failure. The subsequent query may get the stale data, but any query after that will get latest data. This is an extreme edge case, and typically N=5, R=3, W3= will give you full consistency. Reading from three nodes isn't actually that much of an overhead. For a query with R=3, the client would make that request to the node it's connected to (the coordinator). The coordinator will query replicas in parallel (not sequenctially). It willmerge up the results with LWW to get the result (and issue read repairs etc. if needed). As the queries happen in parallel, the overhead is greatly reduced.
Yes.
This is a matter of data modelling. You describe one approach (though partitioning on birthday rather than dept might be better and result in more even distribution of partitions). Do you need the employee and department tables...are they needed for other queries? If not, maybe you just need one. If you denormalize, you'll need to maintain the data manually. In Cassandra 3.0, global indexes will allow you to query on an index without being inefficient (which is the case when using a secondary index without specifying the partition key today). Yes another option is to partition employeed by birthday and do two queries, and do the join in memory in the client. Cassandra queries hitting a partition are very fast, so doing two won't really be that expensive.
I noticed if I have a java method in which I have a preparedStatement uisng the JDBC driver that comes with Cassandra it is always slow. But if I put the same query twice in the method the second time it is 20x faster. Why is that? I would think the second, third, four time I call the java method it would be faster then the first. I am using Cassandra 1.2.5. I have also cached 100MB of rows in the row-cache and set the table to caching = "all". In Cassandra-cli I verified the settings. And in Cassandra-Cli I verified the second, third fourth time I get the rows from the same table I do the JDBC calls against I get faster response time.
Any Ideas?
Thanks,
-Tony
From the all knowing CQL3 documentation (always a great starting point btw):
Prepared statement is an optimization that allows to parse a query only once but execute it multiple times with different concrete values.
The statement gets cached. This is the difference maker you are experiencing. Also prepared statements get pre-compiled, typically meaning an execution plan is prepared before the query is run against the db. Knowing what you are doing makes the process faster.
At the first run your prepared statement is cached in-case you run the same query again, which you do, and since its cached the querying will be executed much faster.