I implemented a connection pool (poolMin=poolMax=10) with node-oracledb and i saw a difference of up to 100 times especially in case of few users like 10. Really impressive. I also increased UV_THREADPOOL_SIZE like 4 + poolMax. At this point I could not understand somethings.
process.env.UV_THREADPOOL_SIZE = 4 + config.pool.poolMax // Default + Max
NodeJs works as Single Thread (with additional 4 threads those none of them are used for network I/O). So when i use a pool with 10 connections, can Single Thread use all of these connections? Or isn't it Single Thread with these settings anymore? Because i added 10 more to UV_THREADPOOL_SIZE. I would be grateful to anyone who explained this matter.
Btw, I wonder if using a fixed number pool like 10 would cause a problem in case of too many users? For example, if the number of instant users is 500, we can reach 5000 instant users on certain days of the year. Do I need to make a special setting (e.g. pool size 100) for those days or will the default be enough?
Thanks in advace.
When you do something like connection.execute(), that work will handled by a Node.js worker thread until the call completes. And each underlaying Oracle connection can only ever do one 'thing' (like execute, or fetch LOB data) at a time - this is a fundamental (i.e. insurmountable) behavior of Oracle connections.
For node-oracledb you want the number of worker threads to be at least as big as the number of connections in the connection pool, plus some extra for non database work. This allows connections to do their thing without blocking any other connection.
Any use of Promise.all() (and similar constructs) using a single connection should be assessed and considered for rewriting as a simple loop. Prior to node-oracledb 5.2, each of the 'parallel' operations for Promise.all() on a single connection will use a thread but this will be blocked waiting for prior work on the connection to complete, so you might need even more threads available. From 5.2 onwards any 'parallel' operations on a single connection will be queued in the JavaScript layer of node-oracledb and will be executed sequentially, so you will only need a worker thread per connection at most. In either version, using Promise.all() where each unit of work has its own connection is different, and only subject to the one-connection per thread requirements.
Check the node-oracledb documentation Connections, Threads, and Parallelism and Connection Pool Sizing.
Separate to how connections are used, first you have to get a connection. Node-oracledb will queue connection pool requests (e.g. pool.getConnection()) if every connection in the pool is already in use. This provides some resiliency under connection spikes. There are some limits to help real storms: queueMax and queueTimeout. Yes, at peak periods you might need to increase the poolMax value. You can check the pool statistics to see pool behavior. You don't want to make the pool too big - see the doc.
Side note: process.env.UV_THREADPOOL_SIZE doesn't have an effect in Node.js on Windows; the UV_THREADPOOL_SIZE variable must be set before Node.js is started.
Related
I am using Knex version 0.21.15 npm. my pooling parameter is pool {min: 3 , max:300}.
Oracle is my data base server.
pool Is this pool count or session count?
If it is pool, how many sessions can create using a single pool?
If i run one non transaction query 10 time using knex connection ,how many sessions will create?
And when the created session will cleared from oracle session?
Is there any parameter available to remove the idle session from oracle.?
suggest me please if any.
WARNING: a pool.max value of 300 is far too large. You really don't want the database administrator running your Oracle server to distrust you: that can make your work life much more difficult. And such a large max pool size can bring the Oracle server to its knees.
It's a paradox: often you can get better throughput from a database application by reducing the pool size. That's because many concurrent queries can clog the database system.
The pool object here governs how many connections may be in the pool at once. Each connection is a so-called serially reusable resource. That is, when some part of your nodejs program needs to run a query or series of queries, it grabs a connection from the pool. If no connection is already available in the pool, the pooling stuff in knex opens a new one.
If the number of open connections is already at the pool.max value, the pooling stuff makes that part of your nodejs program wait until some other part of the program finishes using a connection in the pool.
When your part of the nodejs program finishes its queries, it releases the connection back to the pool to be reused when some other part of the program needs it.
This is almost absurdly complex. Why bother? Because it's expensive to open connections and much cheaper to re-use them.
Now to your questions:
pool Is this pool count or session count?
It is a pair of limits (min / max) on the count of connections (sessions) open within the pool at one time.
If it is pool, how many sessions can create using a single pool?
Up to the pool.max value.
If i run one non transaction query 10 time using knex connection ,how many sessions will create?
It depends on concurrency. If your tenth query before the first one completes, you may use ten connections from the pool. But you will most likely use fewer than that.
And when the created session will cleared from oracle session?
As mentioned, the pool keeps up to pool.max connections open. That's why 300 is too many.
Is there any parameter available to remove the idle session from oracle.?
This operation is called "evicting" connections from the pool. knex does not support this. Oracle itself may drop idle connections after a timeout. Ask your DBA about that.
In the meantime, use the knex defaults of pool: {min: 2, max: 10} unless and until you really understand pooling and the required concurrency of your application. max:300 would only be justified under very special circumstances.
I read that Redis is Single Thread.
Using jedis client (java) we can configure pool connections, like as:
spring.redis.jedis.pool.max-active=8 # Maximum number of connections that can be allocated by the pool at a given time. Use a negative value for no limit.
spring.redis.jedis.pool.max-idle=8 # Maximum number of "idle" connections in the pool. Use a negative value to indicate an unlimited number of idle connections.
spring.redis.jedis.pool.max-wait=-1ms # Maximum amount of time a connection allocation should block before throwing an exception when the pool is exhausted. Use a negative value to block indefinitely.
spring.redis.jedis.pool.min-idle=0 # Target for the minimum number of idle connections to maintain in the pool. This setting only has an effect if it is positive.
I know that pool connection is important to get a connect already opened, therefore not spending time to connect again.
Imagine that 8 "client request" uses all available pools, so 8 connections will be used, these clients do a "GET" command in Redis.
Redis will be process one thread per time? Each thread needs wait for other to finish since Redis is Single Thread? In this case when 1 "GET" it is processing other 7 are in Redis queue?
How max-active impacts Redis performance if Redis is Single Thread?
Only main loop of Redis is single thread. It uses client buffers in separate threads to speed up processing. Most of the time you spend getting data from Redis is networking time. They help with that. This means unless you are storing really big objects, you will get almost linear throughput increase.
Here I measured different setups for Lettuce (another client). I expect 'pooled' setup to be very similar to what you will get with Jedis: Redis is single thread. Then why should I use lettuce?
I feel like this question would have been asked before, but I can't find one. Pardon me if this is a repeat.
I'm building a service on Node.js hosted in Heroku and using MongoDB hosted by Compose. Under heavy load, the latency is most likely to come from the database, as there is nothing very CPU-heavy in the service layer. Thus, when MongoDB is overloaded, I want to return an HTTP 503 promptly instead of waiting for a timeout.
I'm also using REDIS, and REDIS has a feature where you can check the number of queued commands (redisClient.command_queue.length). With this feature, I can know right away if REDIS is backed up. Is there something similar for MongoDB?
The best option I have found so far is polling the server for status via this command, but (1) I'm hoping for something client side, as there could be spikes within the polling interval that cause problems, and (2) I'm not actually sure what part of the status response I want to act on. That second part brings me to a follow up question...
I don't fully understand how the MondoDB client works with the server. Is one connection shared per client instance (and in my case, per process)? Are queries and writes queued locally or on the server? Or, is one connection opened for each query/write, until the database's connection pool is exhausted? If the latter is the case, it seems like I might want to keep an eye on the open connections. Does the MongoDB server return such information at other times, besides when polled for status?
Thanks!
MongoDB connection pool workflow-
Every MongoClient instance has a built-in connection pool. The client opens sockets on demand to support the number of concurrent MongoDB operations your application requires. There is no thread-affinity for sockets.
The client instance, opens one additional socket per server in your MongoDB topology for monitoring the server’s state.
The size of each connection pool is capped at maxPoolSize, which defaults to 100.
When a thread in your application begins an operation on MongoDB, if all other sockets are in use and the pool has reached its maximum, the thread pauses, waiting for a socket to be returned to the pool by another thread.
You can increase maxPoolSize:
client = MongoClient(host, port, maxPoolSize=200)
By default, any number of threads are allowed to wait for sockets to become available, and they can wait any length of time. Override waitQueueMultiple to cap the number of waiting threads. E.g., to keep the number of waiters less than or equal to 500:
client = MongoClient(host, port, maxPoolSize=50, waitQueueMultiple=10)
Once the pool reaches its max size, additional threads are allowed to wait indefinitely for sockets to become available, unless you set waitQueueTimeoutMS:
client = MongoClient(host, port, waitQueueTimeoutMS=100)
Reference for connection pooling-
http://blog.mongolab.com/2013/11/deep-dive-into-connection-pooling/
Working with the Node-MySql module:
From my understanding multithreaded programs benefit more from pooling connections than singlethreaded ones. Is this true?
And if this logic proves true, what scenario is connection pooling beneficial in a Node.JS application?
Whether single or multithreaded, pooling can still be beneficial in allowing open connections to be reused rather than being closed only to open another immediately after:
When you are done with a connection, just call connection.release() and the connection will return to the pool, ready to be used again by someone else.
The added benefit with multithreading is that the pool can also manage multiple, concurrent connections:
Connections are lazily created by the pool. If you configure the pool to allow up to 100 connections, but only ever use 5 simultaneously, only 5 connections will be made.
Though, to be clear, Node is multithreaded. It just uses a different model than seems to be typical -- 1 "application" thread which executes JavaScript and multiple "worker" threads handling the brunt of asynchronous I/O.
It seems like the classical way to handle transactions with JDBC is to set auto-commit to false. This creates a new transaction, and each call to commit marks the beginning the next transactions.
On multithreading app, I understand that it is common practice to open a new connection for each thread.
I am writing a RMI based multi-client server application, so that basically my server is seamlessly spawning one thread for each new connection.
To handle transactions correctly should I go and create a new connection for each of those thread ?
Isn't the cost of such an architecture prohibitive?
Yes, in general you need to create a new connection for each thread. You don't have control over how the operating system timeslices execution of threads (notwithstanding defining your own critical sections), so you could inadvertently have multiple threads trying to send data down that one pipe.
Note the same applies to any network communications. If you had two threads trying to share one socket with an HTTP connection, for instance.
Thread 1 makes a request
Thread 2 makes a request
Thread 1 reads bytes from the socket, unwittingly reading the response from thread 2's request
If you wrapped all your transactions in critical sections, and therefore lock out any other threads for an entire begin/commit cycle, then you might be able to share a database connection between threads. But I wouldn't do that even then, unless you really have innate knowledge of the JDBC protocol.
If most of your threads have infrequent need for database connections (or no need at all), you might be able to designate one thread to do your database work, and have other threads queue their requests to that one thread. That would reduce the overhead of so many connections. But you'll have to figure out how to manage connections per thread in your environment (or ask another specific question about that on StackOverflow).
update: To answer your question in the comment, most database brands don't support multiple concurrent transactions on a single connection (InterBase/Firebird is the only exception I know of).
It'd be nice to have a separate transaction object, and to be able to start and commit multiple transactions per connection. But vendors simply don't support it.
Likewise, standard vendor-independent APIs like JDBC and ODBC make the same assumption, that transaction state is merely a property of the connection object.
It's uncommon practice to open a new connection for each thread.
Usually you use a connection pool like c3po library.
If you are in an application server, or using Hibernate for example, look at the documentation and you will find how to configure the connection pool.
The same connection object can be used to create multiple statement objects and these statement objects can then used by different threads concurrently. Most modern DBs interfaced by JDBC can do that. The JDBC is thus able to make use of concurrent cursors as follows. PostgreSQL is no exception here, see for example:
http://doc.postgresintl.com/jdbc/ch10.html
This allows connection pooling where the connection are only used for a short time, namely to created the statement object and but after that returned to the pool. This short time pooling is only recommended when the JDBC connection does also parallelization of statement operations, otherwise normal connection pooling might show better results. Anyhow the thread can continue work with the statement object and close it later, but not the connection.
1. Thread 1 opens statement
3. Thread 2 opens statement
4. Thread 1 does something Thread 2 does something
5. ... ...
6. Thread 1 closes statement ...
7. Thread 2 closes statement
The above only works in auto commit mode. If transactions are needed there is still no need to tie the transaction to a thread. You can just partition the pooling along the transactions that is all and use the same approach as above. But this is only needed not because of some socket connection limitation but because the JDBC then equates the session ID with the transaction ID.
If I remember well there should be APIs and products around with a less simplistic design, where teh session ID and the transaction ID are not equated. In this APIs you could write your server with one single database connection object, even when it does
transactions. Will need to check and tell you later what this APIs and products are.