I read this sentence from couchbase document: "Both the document data and the view index information are written to storage in an append-only format. Changes to the document data and index updates create fragmentation on storage of the active data."
Can you explain append-only format in store data on disk.
Related
To put it in context, I have a bucket where I storage CSV files and a function that works to put that Data into a Database when you load new CSV into the bucket.
I try to upload 100 CSV at the same time, in all, 581.100 records (70 MB)
All of those files appears in my bucket and a new table is created.
But when I do a “select count” I only found 267306 records (46 % of the total)
I try to do it again, different bucket, function, and table, I try to upload another 100 files, 4.779.100 records this time (312 MB)
When I check the table in big query I realize that only 2.293.920 records exist (47,9%) of the one that supposedly exist.
So my question is, is there a way in which I can upload all the CSV that I want without losing data? Or does GCP have some restriction for that task?
Thank you.
As pointed out in your last comment:
google.api_core.exceptions.Forbidden: 403 Exceeded rate limits: too many table update operations for this table
This error shows that you have reached the limit for maximum rate of table metadata update operations per table for Standard tables, according to the documentation. You can review the limits that may apply here. Note that this quota cannot be increased.
In the diagnosis section, it says:
Metadata table updates can originate from API calls that modify a table's metadata or from jobs that modify a table's content.
As a resolution, you can do the following:
Reduce the update rate for the table metadata.
Add a delay between jobs or table operations to make sure that the update rate is within the limit.
For data inserts or modification, consider using DML operations. DML operations are not affected by the Maximum rate of table metadata update operations per table rate limit.
DML operations have other limits and quotas. For more information, see Using data manipulation language (DML).
If you frequently load data from multiple small files stored in Cloud Storage that uses a job per file, then combine multiple load jobs into a single job. You can load from multiple Cloud Storage URIs with a comma-separated list (for example, gs://my_path/file_1,gs://my_path/file_2), or by using wildcards (for example, gs://my_path/*).
For more information, see Batch loading data.
If you use single-row queries (that is, INSERT statements) to write data to a table, consider batching multiple queries into one to reduce the number of jobs. BigQuery doesn't perform well when used as a relational database, so single-row INSERT statements executed at a high speed is not a recommended best practice.
If you intend to insert data at a high rate, consider using BigQuery Storage Write API. It is a recommended solution for high-performance data ingestion. The BigQuery Storage Write API has robust features, including exactly-once delivery semantics. To learn about limits and quotas, see Storage Write API and to see costs of using this API, see BigQuery data ingestion pricing.
I know how B+tree works in memory but I'm confused how it is used by database like MySQL.
Without any optimization, tree nodes(leaf or non-leaf) should be save to disk if any data is updated/inserted and should be loaded from disk if someone search.
How B+tree nodes are serialized into one file on disk ? Random-access in disk seems inevitable.
Yes, random access happens continually in InnoDB. The B+Tree data structure for indexes is written to many pages in a tablespace, not necessarily consecutive pages. Each page has links to the next page(s), which may be anywhere in the tablespace.
This is mitigated by loading pages into RAM, into the innodb buffer pool, where random access does not incur overhead.
If you're interested in details about how InnoDB stores indexes on pages, I suggest studying Jeremy Cole's series of blog posts: https://blog.jcole.us/innodb/
Does Geomesa provide ability to create HBase table snapshots? If yes, then how it works with primary and index table? To ensure index table and primary table are in sync, What it does?
GeoMesa does not provide any mechanism to take snapshots in HBase, however, the standard HBase snapshot mechanisms work fine. As long as you're not performing any administrative operations on GeoMesa while taking the snapshots there won't be any issues around keeping the GeoMesa metadata table and the index tables in sync.
I have a data table which has to be read often. I need to store in it strings and binary data of variable length. I could store data as BLOB or TEXT, but the way I understand MySql, those types are stored on the hard drive instead of memory, and if I use them, the speed of reading the table is going to be low.
Are there any alternative variable length types which I could use? Or, maybe, is there a way to tell MySql to hold the data in columns of those types in memory?
Is this 'data table' the only place that the strings are stored? If so, you need the 'persistence' of storing it on disk. However, MySQL will "cache" the data, so reads will almost always be from RAM.
If each element of data is not 'too' big, you could use ENGINE=MEMORY for the table; that would leave the data only in RAM. A system crash would lose the data.
But if you don't need persistence, there are many flavors of caching outside MySQL. Please describe where the data comes from, what language is using the data, how big the data is, etc.
Well, I know already that:
1. InnoDB is faster for data insertion but slower on data retrieval.
2. MyISAM is faster for data retrieval but slower for data insertion.
My situation is a bit different, and I just cant figure out what settings are good for me, let me explain:
My software inserts each user's hit's data (IP, Host, Referral data etc) to a Logs table at run-time. Previously, I used to write this data to a .csv file and then import it to the DB after predefined minutes/hours, but it was not good for me, I need real-time data.
I have several auto processes that run each minute, getting data from the Logs table, hence I need this to be fast.
My question is, what type of MySQL engine should I use for the Logs table, InnoDB or MyISAM?
currently, I'm using InnoDB cause it's faster for insertion, however, should I leave it this way, or switch back to MyISAM?
Thanks
InnoDB is faster for data insertion but slower on data retrieval. 2. MyISAM is faster for data retrieval but slower for data insertion.
Not true. In fact, under most workloads it's just the opposite.
That prevailing wisdom you cite is based on InnoDB of about 2004.
My question is, what type of MySQL engine should I use for the Logs table, InnoDB or MyISAM?
If you care about your data not getting corrupted, use InnoDB.