In columnar storage column data will be stored contagiously and if there is any insert does the shifting of block happens? I believe it is more expensive to shift the block. How is this internally managed ?
Not immediately
This is indeed a very costly operation, so it is mostly delayed, and only performed occasionally.
Changes of each main table (inserts, modifies, updates) are colleced into a delta table, and they are merged into the main table when some preset conditions are met.1
1) If the delta table has more lines than X1, becomes bigger than X2 percent of the main table, X3 time passed since the last merge, etc. All X variables are changeable.
Related
When a PostgreSQL query's execution plan is generated, how does an index's fill factor affect whether the index gets used in favor of a sequential scan?
A fellow dev and I were reviewing the performance of a PostgreSQL (12.4) query with a windowed function of row_number() OVER (PARTITION BY x, y, z) and seeing if we could speed it up with an index on said fields. We found that during the course of the query the index would get used if we created it with a fill factor >= 80 but not at 75. This was a surprise to us as we did not expect the fill factor to be considered in creating the query plan.
If we create the index at 75 and then insert rows, thereby packing the pages > 75, then once again the index gets used. What causes this behavior and should we consider it when selecting an index's fill factor on a table that will have frequent inserts and deletes and be periodically vacuumed?
If we create the index at 75 and then insert rows, thereby packing the pages > 75, then once again the index gets used.
So, it is not the fill factor, but rather the size of the index (which is influenced by the fill factor). This agrees with my memory that index size is a (fairly weak) influence on the cost estimate. That influence is almost zero if you are reading only one tuple, but larger if you area reading many tuples.
If the cost estimates of the plan are close to each other, then small differences such as this will be enough to drive one over the other. But that doesn't mean you should worry about them. If one plan is clearly superior to the other, then you should think about why the estimates are so close together to start with when the realities are not close together.
I am new to the timescale database. I was learning about chunks and how to create chunks based on time.
But there is another time/space chunking which is confusing me a lot. Please help me with below queries.
What is a dimension in a timescale DB?
What is space chunking and how it works?
Thanks in advance.
A dimension in TimescaleDB is associated with a column. Each hypertable requires to define at least a time dimension, which is a time column for the time series. Then a hypertable is divided into chunks, where each chunk contains data for a time interval of the time dimension. As result all new data usually arrives into the latets chunk, while other chunks contain older data.
Then, it is possible to define space dimensions on other columns, for example device column or/and location column. No interval is defined for space dimensions, instead a number of partitions is defined. So for the same time interval, several chunks will be created, which is equivalent to the number of partitions. Data are distributed by a hashing function on the values of the space dimension. For example, if 3 partitions are defined for a space dimension on device column and 12 different device values were present in the data, each space chunk will contain 4 different values with a hash function uniformly distributing the values.
Space dimensions are specifically useful for parallel I/O, when data are stored on several disks. Another scenario is multinode, i.e., distributed version of hypertable (beta feature, which coming to release in 2.0).
There are some complex usage cases when space partitioning will be also helpful.
You can read more in add_dimension docs, cloud KB about space partitioning
A note in the doc:
Supporting more than one additional dimension is currently experimental.
I am new to PostgreSQL and database systems, and I am currently trying to create a database to store observed values as well as all predictions made in the past for some time series.
I have already built a table (actually a view) for observed values, with rows looking basically like:
(time, object, value)
Now I want to store predictions, which means for each time, what has been predicted by some software for the following next N time steps, N being variable since the software has different prediction types.
I have thought about multiple solutions, which are the following:
Store each prediction as a row, using max(N)=240 columns i.e (time, object, value 1, value 2, ..., value 240).
Store each prediction as a row, with the prediction values as a binary JSON, i.e (time, object, JSONB prediction).
Store each prediction value as a row, with a column specifying the delay of the prediction in hours, i.e
(time, object, delay, value).
I don't know how each of these choices would affect performance when I will retrieve and compute summary values on the predictions. A typical thing I would like to do is to retrieve the performance of the prediction for some delay, i.e. how big is the prediction error when we predict x days ahead, and I need this query to be executed pretty fast, to display it in a dashboard.
Which choice do you think is the best? Or do you have any other idea?
Thanks a lot!
Without further information about the access patterns for the collected data i would strongly recommend to use jsonb.
Using one column per timestep will result in bloat of the system catalog and statistics.
If you need to filter on the values of the predictions, you don't want to maintain 240 indexes also.
If you don't need to use these values within a WHERE condition you may use json instead of jsonb.
I have a table that will have about 3 * 10 ^ 12 lines (3 trillion), but with only 3 attributes.
In this table you will have the IDs of 2 individuals and the similarity between them (it is a number between 0 and 1 that I multiplied by 100 and put as a smallint to decrease the space).
It turns out that I need to perform, for a certain individual that I want to do the research, the summarization of these columns and returning how many individuals have up to 10% similarity, 20%, 30%. These values are fixed (every 10) until identical individuals (100%).
However, as you may know, the query will be very slow, so I thought about:
Create a new table to save summarized values
Create a VIEW to save these values.
As individuals are about 1.7 million, the search would not be so time consuming (if indexed, returns quite fast). So, what can I do?
I would like to point out that my population will be almost fixed (after the DB is fully populated, it is expected that almost no increase will be made).
A view won't help, but a materialized view sounds like it would fit the bill, if you can afford a sequential scan of the large table whenever the materialized view gets updated.
It should probably contain a row per user with a count for each percentile range.
Alternatively, you could store the aggregated data in an independent table that is updated by a trigger on the large table whenever something changes there.
after checking a lot of similar questions on stackoverflow, it seems that context will tell which way is the best to hold the data...
Short story, I add over 10,000 new rows of data in a very simple table containing only 3 columns. I will NEVER update the rows, only doing selects, grouping and making averages. I'm looking for the best way of storing this data to make the average calculations as fast as possible.
To put you in context, I'm analyzing a recorded audio file (Pink Noise playback in a sound mixing studio) using FFTs. The results for a single audio file is always in the same format: The frequency bin's ID (integer) and its value in decibels (float value). I'm want to store these values in a PostgreSQL DB.
Each bin (band) of frequencies (width = 8Hz) gets an amplitude in decibels. The first bin is ignored, so it goes like this (not actual dB values):
bin 1: 8Hz-16Hz, -85.0dB
bin 2: 16Hz-32Hz, -73.0dB
bin 3: 32Hz-40Hz, -65.0dB
...
bin 2499: 20,000Hz-20,008Hz, -49.0dB
The goal is to store an amplitude of each bin from 8Hz through 20,008Hz (1 bin covers 8Hz).
Many rows approach
For each analyzed audio file, there would be 2,499 rows of 3 columns: "Analysis UID", "Bin ID" and "dB".
For each studio (4), there is one recording daily that is to be appended in the database (that's 4 times 2,499 = 9,996 new rows per day).
After a recording in one studio, the new 2,499 rows are used to show a plot of the frequency response.
My concern is that we also need to make a plot of the averaged dB values of every bin in a single studio for 5-30 days, to see if the frequency response tends to change significantly over time (thus telling us that a calibration is needed in a studio).
I came up with the following data structure for the many rows approach:
"analysis" table:
analysisUID (serial)
studioUID (Foreign key)
analysisTimestamp
"analysis_results" table:
analysisUID (Foreign key)
freq_bin_id (integer)
amplitude_dB (float)
Is this the optimal way of storing data? A single table holding close to 10,000 new rows a day and making averages of 5 or more analysis, grouping by analysisUIDs and freq_bin_ids? That would give me 2,499 rows (each corresponding to a bin and giving me the averaged dB value).
Many columns approach:
I thought I could do it the other way around, breaking the frequency bins in 4 tables (Low, Med Low, Med High, High). Since Postgres documentation says the column limit is "250 - 1600 depending on column types", it would be realistic to make 4 tables containing around 625 columns (2,499 / 4) each representing a bin and containing the "dB" value, like so:
"low" table:
analysisUID (Foreign key)
freq_bin_id_1_amplitude_dB (float)
freq_bin_id_2_amplitude_dB (float)
...
freq_bin_id_625_amplitude_dB (float)
"med_low" table:
analysisUID (Foreign key)
freq_bin_id_626_amplitude_dB (float)
freq_bin_id_627_amplitude_dB (float)
...
freq_bin_id_1250_amplitude_dB (float)
etc...
Would the averages be computed faster if the server only has to Group by analysisUIDs and make averages of each column?
Rows are not going to be an issue, however, the way in which you insert said rows could be. If insert time is one of the primary concerns, then make sure you can bulk insert them OR go for a format with fewer rows.
You can potentially store all the data in a jsonb format, especially since you will not be doing any updates to the data-- it may be convenient to store it all in one table at a time, however the performance may be less.
In any case, since you're not updating the data, the (usually default) fillfactor of 100 is appropriate.
I would NOT use the "many column" approach, as the
amount of data you're talking about really isn't that much. Using your first example of 2 tables and few columns is very likely the optimal way to do your results.
It may be useful to index the following columns:
analysis_results.freq_bin_id
analysis.analysisTimestamp
As to breaking the data into different sections, it'll depend on what types of queries you're running. If you're looking at ALL freq bins, using multiple tables will just be a hassle and net you nothing.
If only querying at some freq_bin's at a time, it could theoretically help, however, you're basically doing table partitions and once you've moved into that land, you might as well make a partition for each frequency band.
If I were you, I'd create your first table structure, fill it with 30 days worth of data and query away. You may (as we often do) be overanalyzing the situation. Postgres can be very, very fast.
Remember, the raw data you're analyzing is something on the order of a few (5 or less) meg per day at an absolute maximum. Analyzing 150 mb of data is no sweat for a DB running with modern hardware if it's indexed and stored properly.
The optimizer is going to find the correct rows in the "smaller" table really, really fast and likely cache all of those, then go looking for the child rows, and it'll know exactly what ID's and ranges to search for. If your data is all inserted in chronological order, there's a good chance it'll read it all in very few reads with very few seeks.
My main concern is with the insert speed, as a doing 10,000 inserts can take a while if you're not doing bulk inserts.
Since the measurements seem well behaved, you could use an array, using the freq_bin as an index (Note: indices are 1-based in sql)
This has the additional advantage of the aray being stored in toasted storage, keeping the fysical table small.
CREATE TABLE herrie
( analysisUID serial NOT NULL PRIMARY KEY
, studioUID INTEGER NOT NULL REFERENCES studio(studioUID)
, analysisTimestamp TIMESTAMP NOT NULL
, decibels float[] -- array with 625 measurements
, UNIQUE (studioUID,analysisTimestamp)
);