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Insert 2 Million rows into timscaldb with python-dataframes

i want to insdert About 2 Million rows from a csv into postgersql.
There are 2 ways.
With dataframes in python
or directly with Import csv in PostgreSQL
The python way:
engine = create_engine("postgresql+psycopg2://postgres:passwd#127.0.0.1/postgres")
con = engine.connect()
df = pd.read_csv(r"C:\2million.csv",delimiter=',',names=['x','y'],skiprows=1)
df.to_sql(name='tablename',con=con,schema='timeseries',if_exists='append',index=False)
print("stored")
Took 800 seconds to insert.
the way with directly Import in PostgreSQL took just 10 seconds.
I thought, that the inserttime with timescaledb is much faster than 800 seconds, for inserting 2Million rows.
Or is the way i am trying to insert the rows simply the limiting factor ?

I'm not an expert in timescaledb, but I don't think it does anything merely by being installed. You have to invoke it on each table you want to use it for, and you are not doing that. So you are just using plain PostgreSQL here.
Pandas' to_sql is infamously slow. By default it inserts one row per INSERT statement, which is quite bad for performance. If you are using a newer version of pandas (>=0.24.0), you could specify to_sql(...,method='multi',chunksize=10000) to make is suck a bit less by specifying multiple rows per INSERT statement. I think pandas implemented it this way, rather than using bulk import, because every database system does bulk import differently.
You are fundamentally misusing pandas. It is data analysis library, not a database bulk loader library. Not only do you not take advantage of database-specific bulk import features, but you are parsing the entire csv file into an in-memory dataframe before you start writing any of it to the database.

here's one way of doing it that's much faster than pandas' to_sql
(python) df.to_csv('tmp.csv')
(sql) COPY foobar FROM 'tmp.csv' DELIMITER ',' CSV HEADER;

Is there a way to set columns to null within dask read_sql_table?

I'm connecting to an oracle database and trying to bring across a table with roughly 77 million rows. At first I tried using chunksize in pandas but I always got a memory error no matter what chunksize I set. I then tried using Dask since I know its better for large amounts of data. However, there're some columns that need to be made NULL, is there away to do this within read_sql_table query like there is in pandas when you can write out your sql query?
Cheers

If possible, I recommend setting this up on the oracle side, making a view with the correct data types, and using read_sql_table with that.
You might be able to do it directly, since read_sql_table accepts sqlalchemy expressions. If you can phrase it as such, it ought to work.

Improve CSV push to MySQL with virtual files in python

I am trying to find a way to improve the speed while pushing data to a MySQL database using pandas in python.
After my performance tests I arrived to the same conclusion that other people did: the best way to push data to a MySQL database is to use the native query 'LOAD DATA INFILE..." instead of the to_sql pandas method (even with improvements like this one or this one).
My problem is that when I want to push my data, it is in memory. So in order to use the native MySQL query, I need to dump it first into a file on the disk and then use the 'LOAD DATA...' query.
So here my question, is there a way to 'simulate' a file written on the disk so i can avoid dumping my big files (200MB+) on it ?
It might happen that dumping a big file can take some minutes, so I would not want to lose too much time there...

This approach may be a viable alternative without touching disk (for the load file):
Write code to create multi-row INSERT statements and execute them. Suggest 1000 rows at a time, with autocommit=ON.

Copying data from postgres to ZODB using pandas - read_csv or read_sql or blaze?

I am creating a new application which uses ZODB and I need to import legacy data mainly from a postgres database but also from some csv files. There is a limited amount of manipulation needed to the data (sql joins to merge linked tables and create properties, change names of some properties, deal with empty columns etc).
With a subset of the postgres data I did a dump to csv files of all the relevant tables, read these into pandas dataframes and did the manipulation. This works but there are errors which are partly due to transferring the data into a csv first.
I now want to load all of the data in (and get rid of the errors). I am wondering if it makes sense to connect directly to the database and use read_sql or to carry on using the csv files.
The largest table (csv file) is only 8MB so I shouldn't have memory issues, I hope. Most of the errors are to do with encoding and or choice of separator (the data contains |,;,: and ').
Any advice? I have also read about something called Blaze and wonder if I should actually be using that.

If your CSV files aren't very large (as you say) then I'd try loading everything into postgres with odo, then using blaze to perform the operations, then finally dumping to a format that ZODB can understand. I wouldn't worry about the performance of operations like join inside the database versus in memory at the scale you're talking about.
Here's some example code:
from blaze import odo, Data, join
for csv, tablename in zip(csvs, tablenames):
odo(csv, 'postgresql://localhost/db::%s' % tablename)
db = Data('postgresql://localhost/db')
# see the link above for more operations
expr = join(db.table1, db.table2, 'column_to_join_on')
# execute `expr` and dump the result to a CSV file for loading into ZODB
odo(expr, 'joined.csv')

How to export a large table (100M+ rows) to a text file?

I have a database with a large table containing more that a hundred million rows. I want to export this data (after some transformation, like joining this table with a few others, cleaning some fields, etc.) and store it int a big text file, for later processing with Hadoop.
So far, I tried two things:
Using Python, I browse the table by chunks (typically 10'000 records at a time) using this subquery trick, perform the transformation on each row and write directly to a text file. The trick helps, but the LIMIT becomes slower and slower as the export progresses. I have not been able to export the full table with this.
Using the mysql command-line tool, I tried to output the result of my query in CSV form to a text file directly. Because of the size, it ran out of memory and crashed.
I am currently investigating Sqoop as a tool to import the data directly to HDFS, but I was wondering how other people handle such large-scale exports?

Memory issues point towards using the wrong database query machanism.
Normally, it is advisable to use mysql_store_result() on C level, which corresponds to having a Cursor or DictCursor on Python level. This ensures that the database is free again as soon as possible and the client can do with thedata whatever he wants.
But it is not suitable for large amounts of data, as the data is cached in the client process. This can be very memory consuming.
In this case, it may be better to use mysql_use_result() (C) resp. SSCursor / SSDictCursor (Python). This limits you to have to take the whole result set and doing nothing else with the database connection in the meanwhile. But it saves your client process a lot of memory. With the mysql CLI, you would achieve this with the -q argument.

I don't know what query exactly you have used because you have not given it here, but I suppose you're specifying the limit and offset. This are quite quick queries at begin of data, but are going very slow.
If you have unique column such as ID, you can fetch only the first N row, but modify the query clause:
WHERE ID > (last_id)
This would use index and would be acceptably fast.
However, it should be generally faster to do simply
SELECT * FROM table
and open cursor for such query, with reasonable big fetch size.