Have you guys ever faced an issue while saving results (export) of a GA4 query in BigQuery as JSONL (delimited) where columns containing NULLS were removed in the JSON downloaded; which can also be noticed post uploading it as a table (upload -> JSONL) and querying. Yet another thing I’ve noticed was that the sequence of the columns after importing were different compared to the original table before exporting. Has anyone faced this issue? If so, I’d appreciate how you found a way around and how can one download and re-upload a JSON with the schema integrity.
P.S: If you are wondering why would one download and re-upload a JSONL, it was just to see if an option was viable other than being in the google cloud ecosystem. Also to be a bit more specific about the NULLS being removed, I meant let’s say float_value or double_value from GA4 Bigquery export(event_params) being eliminated if they were all NULLs
Thanks a ton in advance
Columns containing NULLs were removed. I expected it to retain the data structure like json local.
The sequence of the columns after importing were different / changed compared to pre-export.
I am unable to import csv table > DATEs columns to BigQuery,
DATEs are not recognized, even they have correct format according this docu
https://cloud.google.com/bigquery/docs/schema-detect YYYY-MM-DD
So DATEs columns are not recognized and are renamed to _2020-0122, 2020-01-23...
Is the issue that DATES are in 1st row as column name ?
But How can I then import dates, when I want use them in TimeSeries Charts (DataStudio) ?
here is sample source csv>
Province/State,Country/Region,Lat,Long,2020-01-22,2020-01-23,2020-01-24,2020-01-25,2020-01-026
Anhui,China,31.8257,117.2264,1,9,15,39,60
Beijing,China,40.1824,116.4142,14,22,36,41,68
Chongqing,China,30.0572,107.874,6,9,27,57,75
Here is ig from Bigquery
If you have finite number of days, you can try unpivot table when using it. See blog post.
otherwise, if you dont know how many day columns in csv file.
choose a unique character as csv delimiter then just load whole file into a single column staging table, then use split function. you'll also need unnest. This approach requires a full scan and will be more expensive, especially when file gets bigger.
The issue is that in column names you cannot have a date type, for this reason when the CSV is imported it takes the dates and transforms them to the format with underscores.
The first way to face the problem would be modifying the CSV file, because any import with the first row as a header will change the date format and then it will be harder to get to date type again. If you have any experience in any programming language you can do the transformation very easily. I can help doing this but I do not know your use case so maybe this is not possible. Where does this CSV come from?
If the CSV previous modification is not possible then the second option is what ktopcuoglu said, importing the whole file as one column and process this using SQL function. This is way harder than the first option and as you import all the data into a single column, all the data will have the same data type, what will be a headache too.
If you could explain where the CSV comes from we may be able to influence it before being ingested by BigQuery. Else, you'll need to deep into SQL a bit.
Hope it helps!
Hi, now I can help you further.
First I found some COVID datasets into the public bigquery datasets. The one you are taking from github is already in BigQuery, but there are many others that may work better for your task such as the one called “covid19_ecdc”, that is inside bigquery-public-data. This last one has the confirmed cases and deaths per date and country so it should be easy to make a time series.
Second, I found an interesting link performing what you meant with python and data studio. It’s a kaggle discussion so you may not be familiar with it, but it deserves a check for sure . Moreover, he is using the dataset you are trying to use.
Hope it helps. Do not hesitate to ask!
I am looking for answers to a particular scenario I am dealing with using MySQL's full text search and json column features.
I am storing unstructured/schema-less data in a json column that I want to enable full text search on. I am sure everybody knows MySQL does not allow FTS on json column. But there are ways around it and I have kind of settled on one but I am not sure what the implications of it will be in the long run.
Many people suggest copying the data that needs searching to a generated column. I cannot do that because, as I said, my data does not have any schema and it is not practical for me to create generated columns for all possibilities, it will defeat the purpose of using json in the first place.
So i thought about using just one generated column that takes the whole data from the source json column and stores it as json literal using MySQL's json_extract(). This appears to be working fine and I am satisfied with the results. I, however, am not sure about one thing. Storing json literal will mean storing other redundant things besides the actual data e.g. property keys/names. Considering how full text search works (inverted index and all), would redundant information like that have any effect on searching when data starts to grow? lets not worry about users trying to search for data using the json keys but instead on whether the inclusion if these keys from every row will affect FTS performance or not?
Last thing, yes I have done my homework, i know there are other better ways to do full text search but i am taking this path because of my situation and i need a really solid justification to look into alternate solutions.
In MySQL 5.7 a new data type for storing JSON data in MySQL tables has been
added. It will obviously be a great change in MySQL. They listed some benefits
Document Validation - Only valid JSON documents can be stored in a
JSON column, so you get automatic validation of your data.
Efficient Access - More importantly, when you store a JSON document in a JSON column, it is not stored as a plain text value. Instead, it is stored
in an optimized binary format that allows for quicker access to object
members and array elements.
Performance - Improve your query
performance by creating indexes on values within the JSON columns.
This can be achieved with “functional indexes” on virtual columns.
Convenience - The additional inline syntax for JSON columns makes it
very natural to integrate Document queries within your SQL. For
example (features.feature is a JSON column): SELECT feature->"$.properties.STREET" AS property_street FROM features WHERE id = 121254;
WOW ! they include some great features. Now it is easier to manipulate data. Now it is possible to store more complex data in column.
So MySQL is now flavored with NoSQL.
Now I can imagine a query for JSON data something like
SELECT * FROM t1
WHERE JSON_EXTRACT(data,"$.series") IN
(
SELECT JSON_EXTRACT(data,"$.inverted")
FROM t1 | {"series": 3, "inverted": 8}
WHERE JSON_EXTRACT(data,"$.inverted")<4 );
So can I store huge small relations in few json colum? Is it good? Does it break normalization. If this is possible then I guess it will act like NoSQL in a MySQL column. I really want to know more about this feature. Pros and cons of MySQL JSON data type.
SELECT * FROM t1
WHERE JSON_EXTRACT(data,"$.series") IN ...
Using a column inside an expression or function like this spoils any chance of the query using an index to help optimize the query. The query shown above is forced to do a table-scan.
The claim about "efficient access" is misleading. It means that after the query examines a row with a JSON document, it can extract a field without having to parse the text of the JSON syntax. But it still takes a table-scan to search for rows. In other words, the query must examine every row.
By analogy, if I'm searching a telephone book for people with first name "Bill", I still have to read every page in the phone book, even if the first names have been highlighted to make it slightly quicker to spot them.
MySQL 5.7 allows you to define a virtual column in the table, and then create an index on the virtual column.
ALTER TABLE t1
ADD COLUMN series AS (JSON_EXTRACT(data, '$.series')),
ADD INDEX (series);
Then if you query the virtual column, it can use the index and avoid the table-scan.
SELECT * FROM t1
WHERE series IN ...
This is nice, but it kind of misses the point of using JSON. The attractive part of using JSON is that it allows you to add new attributes without having to do ALTER TABLE. But it turns out you have to define an extra (virtual) column anyway, if you want to search JSON fields with the help of an index.
But you don't have to define virtual columns and indexes for every field in the JSON document—only those you want to search or sort on. There could be other attributes in the JSON that you only need to extract in the select-list like the following:
SELECT JSON_EXTRACT(data, '$.series') AS series FROM t1
WHERE <other conditions>
I would generally say that this is the best way to use JSON in MySQL. Only in the select-list.
When you reference columns in other clauses (JOIN, WHERE, GROUP BY, HAVING, ORDER BY), it's more efficient to use conventional columns, not fields within JSON documents.
I presented a talk called How to Use JSON in MySQL Wrong at the Percona Live conference in April 2018. I'll update and repeat the talk at Oracle Code One in the fall.
There are other issues with JSON. For example, in my tests it required 2-3 times as much storage space for JSON documents compared to conventional columns storing the same data.
MySQL is promoting their new JSON capabilities aggressively, largely to dissuade people against migrating to MongoDB. But document-oriented data storage like MongoDB is fundamentally a non-relational way of organizing data. It's different from relational. I'm not saying one is better than the other, it's just a different technique, suited to different types of queries.
You should choose to use JSON when JSON makes your queries more efficient.
Don't choose a technology just because it's new, or for the sake of fashion.
Edit: The virtual column implementation in MySQL is supposed to use the index if your WHERE clause uses exactly the same expression as the definition of the virtual column. That is, the following should use the index on the virtual column, since the virtual column is defined AS (JSON_EXTRACT(data,"$.series"))
SELECT * FROM t1
WHERE JSON_EXTRACT(data,"$.series") IN ...
Except I have found by testing this feature that it does NOT work for some reason if the expression is a JSON-extraction function. It works for other types of expressions, just not JSON functions. UPDATE: this reportedly works, finally, in MySQL 5.7.33.
The following from MySQL 5.7 brings sexy back with JSON sounds good to me:
Using the JSON Data Type in MySQL comes with two advantages over
storing JSON strings in a text field:
Data validation. JSON documents will be automatically validated and
invalid documents will produce an error. Improved internal storage
format. The JSON data is converted to a format that allows quick read
access to the data in a structured format. The server is able to
lookup subobjects or nested values by key or index, allowing added
flexibility and performance.
...
Specialised flavours of NoSQL stores
(Document DBs, Key-value stores and Graph DBs) are probably better
options for their specific use cases, but the addition of this
datatype might allow you to reduce complexity of your technology
stack. The price is coupling to MySQL (or compatible) databases. But
that is a non-issue for many users.
Note the language about document validation as it is an important factor. I guess a battery of tests need to be performed for comparisons of the two approaches. Those two being:
Mysql with JSON datatypes
Mysql without
The net has but shallow slideshares as of now on the topic of mysql / json / performance from what I am seeing.
Perhaps your post can be a hub for it. Or perhaps performance is an after thought, not sure, and you are just excited to not create a bunch of tables.
From my experience, JSON implementation at least in MySql 5.7 is not very useful due to its poor performance.
Well, it is not so bad for reading data and validation. However, JSON modification is 10-20 times slower with MySql that with Python or PHP.
Lets imagine very simple JSON:
{ "name": "value" }
Lets suppose we have to convert it to something like that:
{ "name": "value", "newName": "value" }
You can create simple script with Python or PHP that will select all rows and update them one by one. You are not forced to make one huge transaction for it, so other applications will can use the table in parallel. Of course, you can also make one huge transaction if you want, so you'll get guarantee that MySql will perform "all or nothing", but other applications will most probably not be able to use database during transaction execution.
I have 40 millions rows table, and Python script updates it in 3-4 hours.
Now we have MySql JSON, so we don't need Python or PHP anymore, we can do something like that:
UPDATE `JsonTable` SET `JsonColumn` = JSON_SET(`JsonColumn`, "newName", JSON_EXTRACT(`JsonColumn`, "name"))
It looks simple and excellent. However, its speed is 10-20 times slower than Python version, and it is single transaction, so other applications can not modify the table data in parallel.
So, if we want to just duplicate JSON key in 40 millions rows table, we need to not use table at all during 30-40 hours. It has no sence.
About reading data, from my experience direct access to JSON field via JSON_EXTRACT in WHERE is also extremelly slow (much slower that TEXT with LIKE on not indexed column). Virtual generated columns perform much faster, however, if we know our data structure beforehand, we don't need JSON, we can use traditional columns instead. When we use JSON where it is really useful, i. e. when data structure is unknown or changes often (for example, custom plugin settings), virtual column creation on regular basis for any possible new columns doesn't look like good idea.
Python and PHP make JSON validation like a charm, so it is questionable do we need JSON validation on MySql side at all. Why not also validate XML, Microsoft Office documents or check spelling? ;)
I got into this problem recently, and I sum up the following experiences:
1, There isn't a way to solve all questions.
2, You should use the JSON properly.
One case:
I have a table named: CustomField, and it must two columns: name, fields.
name is a localized string, it content should like:
{
"en":"this is English name",
"zh":"this is Chinese name"
...(other languages)
}
And fields should be like this:
[
{
"filed1":"value",
"filed2":"value"
...
},
{
"filed1":"value",
"filed2":"value"
...
}
...
]
As you can see, both the name and the fields can be saved as JSON, and it works!
However, if I use the name to search this table very frequently, what should I do? Use the JSON_CONTAINS,JSON_EXTRACT...? Obviously, it's not a good idea to save it as JSON anymore, we should save it to an independent table:CustomFieldName.
From the above case, I think you should keep these ideas in mind:
Why MYSQL support JSON?
Why you want to use JSON? Did your business logic just need this? Or there is something else?
Never be lazy
Thanks
Strong disagree with some of things that are said in other answers (which, to be fair, was a few years ago).
We have very carefully started to adopt JSON fields with a healthy skepticism. Over time we've been adding this more.
This generally describes the situation we are in:
Like 99% of applications out there, we are not doing things at a massive scale. We work with many different applications and databases, the majority of these are capable of running on modest hardware.
We have processes and know-how in place to make changes if performance does become a problem.
We have a general idea of which tables are going to be large and think carefully about how we optimize queries for them.
We also know in which cases this is not really needed.
We're pretty good at data validation and static typing at the application layer.
Lastly,
When we use JSON for storing complex data, that data is never referenced directly by other tables. We also tend to never need to use them in where clauses in hot paths.
So with all this in mind, using a little JSON field instead of 1 or more tables vastly reduces the complexity of queries and data model. Removing this complexity makes it easier to write certain queries, makes our code simpler and just generally saves time.
Complexity and performance is something that needs to be carefully balanced. JSON fields should not be blindly applied, but for the cases where this works it's fantastic.
'JSON fields don't perform well' is a valid reason to not use JSON fields, if you are at a place where that performance difference matters.
One specific example is that we have a table where we store settings for video transcoding. The settings table has 1 'profile' per row, and the settings themselves have a maximum nesting level of 4 (arrays and objects).
Despite this being a large database overall, there's only a few hundreds of these records in the database. Suggesting to split this into 5 tables would yield no benefit and lots of pain.
This is an extreme example, but we have plenty of others (with more rows) where the decision to use JSON fields is a few years in the past, and hasn't yet caused an issue.
Last point: it is now possible to directly index on JSON fields.
I'm building a very simple MTurk-ish app in Rails. The idea is that people will upload csvs containing whatever columns they want (e.g., some id, name of a user, some piece of text, a link, whatever -- these columns will change from task to task), and these csvs will contain all the information for the MTurk task.
My question is: how would I store these csvs in a database? One way is to store each csv row as a blob of unstructured data in MySQL (i.e., I basically leave each row as a string and stick this into a MySQL column). A maybe better way is to use a NoSQL database like MongoDB, where I don't need a predefined schema.
Suggestions? Which way is better, or is there another option? I am using Rails for this, so options that work well with Rails would be great.
Well you pretty much answered your own question.
Either use a NoSQL Document based database (like MongoDB) or split up the cvs and save it in a 1:n correlation within your database as key value pairs attached to a row and column each. your idea to store blobs isn't quite ideal however as it would restrict you from searching within the columns.