Is there a way to fetch all keys who are about to expire within the next X hours?
I see that the scan method only seem to pattern match, and I can't seem to find any other commands which lets me do this.
Redis does not provide this capability (yet). You can, however, keep a Sorted Set where the elements are the key names and the scores are their expiry timestamp - this will allow you to query (ZRANGEBYSCORE) as you wish, at the price of maintaining that data structure.
AFAIK not possible without a full scan of keys. There is no command or group of commands which can provide that information.
KEYS combined with TTL or PTTL may be the only option, but requires full scan. Redis pipeline will improve the performance.
Related
I can think of two ways of checking existence using redis:
Use the whole database as a 'set', and just SET a key and checking existence by GETing it (or using EXISTS as mentioned in the comment by #Sergio Tulentsev)
Use SADD to add all members to a key and check existence by SISMEMBER
Which one is better? Will it be a problem, compared to the same amount of keys in a single set, if I choose the first method and the number of keys in a database gets larger?
In fact, besides these two methods, you can also use the HASH data structure with HEXISTS command (I'll call this method as the third solution).
All these solutions are fast enough, and it's NOT a problem if you have a large SET, HASH, or keyspace.
So, which one should we use? It depends on lots of things...
Does the key has value?
Keys of both the first and the third solution can have value, while the second solution CANNOT.
So if there's no value for each key, I'd prefer the second solution, i.e. SET solution. Otherwise, you have to use the first or third solution.
Does the value has structure?
If the value is NOT raw string, but a data structure, e.g. LIST, SET. You have to use the first solution, since HASH's value CAN only be raw string.
Do you need to do set operations?
If you need to do intersection, union or diff operations on multiple data sets, you should use the second solution. Redis has built-in commands for these operations, although they might be slow commands.
Memory efficiency consideration
Redis takes more memory-efficient encoding for small SET and HASH. So when you have lots of small data sets, take the second and the third solution can save lots of memory. See this for details.
UPDATE
Do you need to set TTL for these keys?
As #dizzyf points out in the comment, if you need to set TTL for these keys, you have to use the first solution. Because items of HASH and SET DO NOT have expiration property. You can only set TTL for the entire HASH or SET, NOT their elements.
I have a SQL table that is accessed continually but changes very rarely.
The Table is partitioned by UserID and each user has many records in the table.
I want to save database resources and move this table closer to the application in some kind of memory cache.
In process caching is too memory intensive so it needs to be external to the application.
Key Value stores like Redis are proving inefficient due to the overhead of serializing and deserializing the table to and from Redis.
I am looking for something that can store this table (or partitions of data) in memory, but let me query only the information I need without serializing and deserializing large blocks of data for each read.
Is there anything that would provide Out of Process in memory database table that supports queries for high speed caching?
Searching has shown that Apache Ignite might be a possible option, but I am looking for more informed suggestions.
Since it's out-of-process, it has to do serialization and deserialization. The problem you concern is how to reduce the serialization/deserizliation work. If you use Redis' STRING type, you CANNOT reduce these work.
However, You can use HASH to solve the problem: mapping your SQL table to a HASH.
Suppose you have the following table: person: id(varchar), name(varchar), age(int), you can take person id as key, and take name and age as fields. When you want to search someone's name, you only need to get the name field (HGET person-id name), other fields won't be deserialzed.
Ignite is indeed a possible solution for you since you may optimize serialization/deserialization overhead by using internal binary representation for accessing objects' fields. You may refer to this documentation page for more information: https://apacheignite.readme.io/docs/binary-marshaller
Also access overhead may be optimized by disabling copy-on-read option https://apacheignite.readme.io/docs/performance-tips#section-do-not-copy-value-on-read
Data collocation by user id is also possible with Ignite: https://apacheignite.readme.io/docs/affinity-collocation
As the #for_stack said, Hash will be very suitable for your case.
you said that Each user has many rows in db indexed by the user_id and tag_id . So It is that (user_id, tag_id) uniquely specify one row. Every row is functional depends on this tuple, you could use the tuple as the HASH KEY.
For example, if you want save the row (user_id, tag_id, username, age) which values are ("123456", "FDSA", "gsz", 20) into redis, You could do this:
HMSET 123456:FDSA username "gsz" age 30
When you want to query the username with the user_id and tag_id, you could do like this:
HGET 123456:FDSA username
So Every Hash Key will be a combination of user_id and tag_id, if you want the key to be more human readable, you could add a prefix string such as "USERINFO". e.g. : USERINFO:123456:FDSA .
BUT If you want to query with only a user_id and get all rows with this user_id, this method above will be not enough.
And you could build the secondary indexes in redis for you HASH.
as the above said, we use the user_id:tag_id as the HASH key. Because it can unique points to one row. If we want to query all the rows about one user_id.
We could use sorted set to build a secondary indexing to index which Hashes store the info about this user_id.
We could add this in SortedSet:
ZADD user_index 0 123456:FDSA
As above, we set the member to the string of HASH key, and set the score to 0. And the rule is that we should set all score in this zset to 0 and then we could use the lexicographical order to do range query. refer zrangebylex.
E.g. We want to get the all rows about user_id 123456,
ZRANGEBYLEX user_index [123456 (123457
It will return all the HASH key whose prefix are 123456, and then we use this string as HASH key and hget or hmget to retrieve infomation what we want.
[ means inclusive, and ( means exclusive. and why we use 123457? it is obvious. So when we want to get all rows with a user_id, we shoud specify the upper bound to make the user_id string's leftmost char's ascii value plus 1.
More about lex index you could refer the article I mentioned above.
You can try apache mnemonic started by intel. Link -http://incubator.apache.org/projects/mnemonic.html. It supports serdeless features
For a read-dominant workload MySQL MEMORY engine should work fine (writing DMLs lock whole table). This way you don't need to change you data retrieval logic.
Alternatively, if you're okay with changing data retrieval logic, then Redis is also an option. To add to what #GuangshengZuo has described, there's ReJSON Redis dynamically loadable module (for Redis 4+) which implements document-store on top of Redis. It can further relax requirements for marshalling big structures back and forth over the network.
With just 6 principles (which I collected here), it is very easy for a SQL minded person to adapt herself to Redis approach. Briefly they are:
The most important thing is that, don't be afraid to generate lots of key-value pairs. So feel free to store each row of the table in a different key.
Use Redis' hash map data type
Form key name from primary key values of the table by a separator (such as ":")
Store the remaining fields as a hash
When you want to query a single row, directly form the key and retrieve its results
When you want to query a range, use wild char "*" towards your key. But please be aware, scanning keys interrupt other Redis processes. So use this method if you really have to.
The link just gives a simple table example and how to model it in Redis. Following those 6 principles you can continue to think like you do for normal tables. (Of course without some not-so-relevant concepts as CRUD, constraints, relations, etc.)
using Memcache and REDIS combination on top of MYSQL comes to Mind.
I'm new to Redis and I want to use the following scheme:
key: EMPLOYEE_*ID*
value: *EMPLOYEE DATA*
I was thinking of adding a time stamp to the end of the key, but I'm not sure if that'll even help. Basically I want to be able to get a list of employees who are the most stale ie having been updated. What's the best way to accomplish this in Redis?
Keep another key with the data about employees (key names) and the update's timestamp - the best candidate for that is a Sorted Set. To maintain that key's data integrity, you'll have update it with pertinent changes whenever you update one the employees' keys.
With that data structure in place, you can easily get the keys names of the recently-updated employees with the ZRANGE command.
Have you tried to filter by expiration time? You could set the same expiration to all keys and update the expiration each time the key is updated. Then with a LUA script you could iterate through the keys and filter by expiration time. Those with smaller expiration time are those who are not updated.
This would work with some assumptions, it depends on how your system works. Also the approach is O(N) with respect to the number of employees. So if on one side you can save space, it will not scale well with the number of entries and the frequency of scan.
I am using the ServiceStack.Redis client on C#.
I added about 5 million records of type1 using the following pattern a::name::1 and 11 million records of type2 using the pattern b::RecId::1.
Now I am using redis typed client as client = redis.As<String>. I want to retrieve all the keys of type2. I am using the following pattern:
var keys = client.SearchKeys("b::RecID::*");
But it takes forever (approximately 3-5 mins) to retrieve the keys.
Is there any faster and more efficient way to do this?
You should work hard to avoid the need to scan the keyspace. KYES is literally a server stopper, but even if you have SCAN available: don't do that. Now, you could choose to keep the keys of things you have available in a set somewhere, but there is no SRANGE etc - in 2. you'd have to use SMEMBERS, which is still going to need to return a few million records - but at least they will all be available. In later server versions, you have access to SCAN (think: KEYS) and SSCAN (think: SMEMBERS), but ultimately you simply have the issue of wanting millions of rows, which is never free.
If possible, you could mitigate the impact by using a master/slave pair, and running the expensive operations on the slave. At least other clients will be able to do something while you're killing the server.
The keys command in Redis is slow (well, not slow, but time consuming). It also blocks your server from accepting any other command while it's running.
If you really want to iterate over all of your keys take a look at the scan command instead- although I have no idea about ServiceStack for this
You can use the SCAN command, make a loop search, where each search is restricted to a smaller number of keys. For a complete example, refer to this article: http://blog.bossma.cn/csharp/nservicekit-redis-support-scan-solution/
I need to create a table that would contain a slice of data produced by a continuously running process. This process generates messages that contain two mandatory components, among other things: a globally unique message UUID, and a message timestamp.
Those messages would be later retrieved by the UUID.
In addition, on a regular basis I would need to delete all messages from that table that are too old, i.e. whose timestamps are more than X away from the current time.
I've been reading the DynamoDB v2 documentation (e.g. Local Secondary Indexes) trying to figure out how to organize my table and whether or not I need a secondary index to perform searches for messages to delete. There might be a simple answer to my question, but I am somehow confused...
So should I just create a table with the UUID as the hash and messageTimestamp as the range key (together with a "message" attribute that would contain the actual message), and then not create any secondary indices? In the examples that I've seen, the hash was something that was not unique (e.g. ForumName under the above link). In my case, the hash would be unique. I am not sure whether than makes any difference.
And if I create the table with hash and range as described, and without a secondary index, then how would I query for all messages that are in a certain timerange regardless of their UUIDs?
DynamoDB introduced Global Secondary Index which would solve this problem.
http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/GSI.html
We've wrestled with this as well. The best solution we've come up with is to create second table for storing the time series data. To do this:
1) Use the date plus "bucket" id for a hash key
You could just use the date, but then I'm guessing today's date would become a "hot" key - one that is written with excessive frequency. This can create a serious bottleneck, as the total throughput for a particular DynamoDB partition is equal to the total provisioned throughput divided by the number of partitions - that means if all your writes are to a single key (today's key) and you have a throughput of 20 writes per second, then with 20 partitions, your total throughput would be 1 write per second. Any requests beyond this would be throttled. Not a good situation.
The bucket can be a random number from 1 to n, where n should be greater than the number of partitions used by the underlying DB. Determining n is a bit tricky of course because Dynamo does not reveal how many partitions it uses. But we are currently working with the upper limit of 200 based on the example found here. The writeup at this link was the basis for our thinking in coming up with this approach.
2) Use the UUID for the range key
3) Query records by issuing queries for each day and bucket.
This may seem tedious, but it is more efficient than a full scan. Another possibility is to use Elastic Map Reduce jobs, but I have not tried that myself yet so cannot say how easy/effective it is to work with.
We are still figuring this out ourselves, so I'm interested to hear others' comments. I also found this presentation very helpful in thinking through how best to use Dynamo:
Falling In and Out Of Love with Dynamo
-John
In short you can not. All DynamoDB queries MUST contain the primary hash index in the query. Optionally, you can also use the range key and/or a local secondary index. With the current DynamoDB functionality you won't be able to use an LSI as an alternative to the primary index. You also are not able to issue a query with only the range key (you can test this out easily in the AWS Console).
A (costly) workaround that I can think of is to issue a scan of the table, adding filters based on the timestamp value in order to find out which fields to delete. Note that filtering will not reduce the used capacity of the query, as it will parse the whole table.