![]() I do have a question and bear with me if I ask something conceptionally incorrect, but if we were to change that configuration, would we see our database not accepting writes until our secondary caught up in the oplog? I just want to make sure that I’m not sacrificing the health of the primary in order to have the secondary caught up. I’ve seen numerous bits of documentation around write concern, and I think that is the right direction. The concern is that we keep falling that we find ourselves outside of that oplog range. We typically see about 30GB of data ingest daily, but our 450GB oplog is enough to give us 42 hours worth of runway. Filtering out what data we truly need is another discussion as we need to do some cleansing on our data. In the last year, our database has grown from 12TB to 22TB. We do have a ton of data that gets inserted into MongoDB on the daily. Thanks for pointing out the 4.4.8 vulnerabilities and we definitely need to get on 4.4.10 as soon as we can. Should the secondary become offline for some reason, the writes could hang indefinitely waiting for confirmation from the offline secondary (since arbiters cannot acknowledge writes), so some mitigation plan is probably needed for this case. One caveat with majority write concern is that there might be issues with arbiters. In short, using majority write concern means that the write will be acknowledged after it was replicated to the secondary, so there’s less chance of it falling off the oplog. This will allow natural throttling for the inserts so that it won’t overwhelm your replica set. Is this correct? How much data are you inserting?Ī quick fix that I can think of off the top of my head is to use majority write concern for all your writes. So much, in fact, that 450GB of oplog is not enough. ![]() ![]() It seems to me from your description that you have a lot of data inserted into MongoDB almost all the time. "operationTime" : Timestamp(1636981009, 2780)įirst of all, I would encourage you to upgrade as soon as possible to MongoDB 4.4.10 due to critical issues identified in 4.4.8. "heartbeatIntervalMillis" : NumberLong(2000), "writeConcernMajorityJournalDefault" : true, We just currently have a lot of pain points with the current setup that was not designed correctly for the current use case so any feedback/suggestions would be very greatly appreciated! rs.conf(): The network and hardware specs are not a concern between the sites but how can I narrow down on the latency and potential configuration issues? Long term, I think we need to scale out sharding to handle our data intake but we need to at least restore our desired HA configuration so we can work towards the desired endstate. It became even more of an issue when it came to creating indexes as we had to remove our secondary’s voting and priority rights to get our primary back to the expected performance for our application.įrom research, this typically falls down to network speeds/bandwidth, latency, hardware specs, or configuration issues. Our secondary has fallen completely out of sync twice in the last month which is very concerning considering the fact that the oplog give us 40+ hours of runway. Oplog last event time: Mon 06:47:30 GMT-0600 (CST) Oplog first event time: Sat 12:05:09 GMT-0600 (CST) Log length start to end: 153741secs (42.71hrs) Oplog is set to 450GB rs0:SECONDARY> rs.printReplicationInfo() The specs on the primary and secondary are CPU: 28core, 56 thread and 256GB RAM and 10Gbit network interfaces. Site 2: 1 Secondary server, voting, electible We are currently running MongoDB v4.4.8 across our environment. In the last month, we’ve been encountering issues with our HA configuration when it comes to replication of the primary and secondary servers.
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![]() Using Element method first value from Queue: 6 Screenshots Using Peek method first value from Queue: 6 ("using Element method first value from Queue : "+positionElement) ("using Peek method first value from Queue : "+positionpeek) Int positionElement = queueOne.element() Element method returns the first element from the Queue peek method returns the first element from the Queue Peek is used to return the head of the queue when the task is successful or else it returns null if the queue is empty. The queue without taking the element out of the queue of the methods is element () or peek ()Įlement is used to return the head of the queue when the task is successful or else throws an exception if the queue is empty. Remove is used to return the head of the queue when the task is successful or else it returns null if the queue is empty. ("After remove and poll method The queue is: " + queueOne) īefore remove and poll method, the queue is: Īfter remove and poll method, the queue is: Screenshot: ("Removed Element value from Queue : "+positionTwo) remove method to remove top of the element in Queue ("Removed Element value from Queue : "+positionOne) poll method to remove top of the element in Queue ("Before remove and poll method The queue is: " + queueOne) Remove is used to return the head of the queue when the task is successful or else throws an exception if the queue is empty. The elements of Java queue using the methods poll () either Remove () ("Example with Offer method The queue is: " + queueTwo) Įxample with Add method The queue is: Įxample with Offer method The queue is: Screenshot ![]() ![]() QueueTwo.offer("One") // offer method to use insert element ("Example with Add method The queue is: " + queueOne) QueueOne.add(6) // add method to use insert element Queue is a interface it has two methods to add elements It returns true when the task is successful or else its return false. Offer is used to insert a specified element into the queue. It returns true when the task is successful or else it throws an exception. The Queue interface includes two methods that can be used to add elements to a queue.Īdd is used to insert a specified element into the queue. The purpose of the queue interface is to reduce the effort to implement the queue. LinkedList, PriotityQueue, ArrayBlockingQueue, DelayQueue, LinkedBlockingQueue, PriotityBlockingQueue, etc. The classes of implementations of a queue are as follows: In the collection of an interface, the iterable interface gets extended. Java Queue interface enlarges the collection of interfaces. Classes That Implement the Queue Interface These classes are known as parameterized type or class because they accept one or more parameters. As a generic method, the section of parameter type of a generic class can have one or more types of parameters: it can separate commas. This class declaration looks similar to a non-generic class declaration, but a type parameter section follows the class name. The priority queue is also an abstract data type (ADT). It bases removal on the natural order or by the comparator. It enables the perfect insertion of elements and efficient removal of the minimum element. A linked list has three elements:Ī priority queue is a collection of items in which all are of the same types. It interconnects each node to the next node through a memory address link. Implementation of Java is below:Ī linked list is a data structure similar to arrays. The queue is an interface that is required to manifest a concrete implementation of the interface to use. ![]()
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In neurons, potassium ions are maintained at high concentrations within the cell while sodium ions are maintained at high concentrations outside of the cell. The negative charge within the cell is created by the cell membrane being more permeable to potassium ion movement than sodium ion movement. The negative resting membrane potential is created and maintained by increasing the concentration of cations outside the cell (in the extracellular fluid) relative to inside the cell (in the cytoplasm). When the membrane is at rest, K + ions accumulate inside the cell due to a net movement with the concentration gradient. The difference in the number of positively charged potassium ions (K +) inside and outside the cell dominates the resting membrane potential ( Figure 16.10). Because ions cannot simply cross the membrane at will, there are different concentrations of several ions inside and outside the cell, as shown in Table 16.1. If the membrane were equally permeable to all ions, each type of ion would flow across the membrane and the system would reach equilibrium. This voltage is called the resting membrane potential it is caused by differences in the concentrations of ions inside and outside the cell. After activation, they become inactivated for a brief period and will no longer open in response to a signal.Ī neuron at rest is negatively charged: the inside of a cell is approximately 70 millivolts more negative than the outside (−70 mV, note that this number varies by neuron type and by species). Voltage-gated ion channels open in response to changes in membrane voltage. The difference in total charge between the inside and outside of the cell is called the membrane potential. Voltage-gated ion channels regulate the relative concentrations of different ions inside and outside the cell. Ion channels that change their structure in response to voltage changes are called voltage-gated ion channels. These ion channels are sensitive to the environment and can change their shape accordingly. Some ion channels need to be activated in order to open and allow ions to pass into or out of the cell. Ion channels have different configurations: open, closed, and inactive, as illustrated in Figure 16.9. To enter or exit the neuron, ions must pass through special proteins called ion channels that span the membrane. 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