blaze / odo Goto Github PK

migration from blaze

blaze/blaze#582

reposting for ease of use:

from @chdoig

As Spark is becoming a popular backend, it's a common use case for people to want to transfer their datasets in DBs to a Spark/HDFS cluster.

It would be nice to have an easy interface for end-users to transfer their tables in DBs to a Cluster.

into(Spark/HDFS, SQL DBs)

A lot of people are talking now about tachyon, maybe worth taking a look:
http://tachyon-project.org/
http://ampcamp.berkeley.edu/big-data-mini-course/tachyon.html

This might be related with @quasiben work on SparkSQL. Maybe a barrier for people to star using SparkSQL is how they should make that transfer since:

A SchemaRDD can be created from an existing RDD, Parquet file, a JSON dataset, or by running HiveQL against data stored in Apache Hive.

But I'm not able to find how you make that connection from existing SQL DBs:
http://spark.apache.org/docs/latest/sql-programming-guide.html

cc: @mrocklin

blaze issue migration

from @mrocklin

The impyla.bdf.from_hdfs function creates an Impala table from a CSV file or Parquet file already living on HDFS. It would be swell to leverage these for into functions.

cc @quasiben

http://odo.readthedocs.org/en/latest/uri.html

should be clear that the root is implicate on the table if its necessary

e.g.

odo(...,'hdfstore://foo.h5::bar') and odo(...,'hdfstore://foo.h5::/bar') are equivalent

More work to be done with spark and sparksql

#78 (comment)

default False, but when True print out progress chunk (llike on chunk i/n). Useful when doing a long conversion

Sucking data from a database into memory with Python is slow. The iopro module might be nice for a fast SQL -> np.ndarray route for convert.

@convert.register(np.ndarray, sqlalchemy.Table)
def table_to_array(tbl, **kwrags):
    turn table into ndarray and return

Is there any plan to extend odo data migrator to the sas and stata data format ?
For the time being, one have to go through pandas.DataFrame to simply convert these files to HDF5.

By default we ignore the index. We should probably only do this if it's the trivial ordering 0, 1, 2, ... and treat it as data otherwise.

cc @brittainhard

Looks like release 3.0 and master think the version is 0.2.0

See: https://github.com/ContinuumIO/odo/blob/master/odo/__init__.py#L67

The following code is failing for me on Windows, Python 2.7:

>>> from into import into
>>> into(list, 'test.csv')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27\lib\site-packages\multipledispatch\dispatcher.py", line 163, in __call__
    return func(*args, **kwargs)
File "C:\Python27\lib\site-packages\into\into.py", line 79, in into_string_string
    r = resource(b, **kwargs)
File "C:\Python27\lib\site-packages\into\regex.py", line 64, in __call__
    return self.dispatch(s)(s, *args, **kwargs)
File "C:\Python27\lib\site-packages\into\directory.py", line 57, in resource_directory
    subtype = type(resource(one_uri, **kwargs))
File "C:\Python27\lib\site-packages\into\regex.py", line 64, in __call__
    return self.dispatch(s)(s, *args, **kwargs)
...
File "C:\Python27\lib\site-packages\into\regex.py", line 64, in __call__
    return self.dispatch(s)(s, *args, **kwargs)
File "C:\Python27\lib\site-packages\into\directory.py", line 54, in resource_directory
    one_uri = first(glob(uri))
File "C:\Python27\lib\glob.py", line 27, in glob
    return list(iglob(pathname))
File "C:\Python27\lib\glob.py", line 38, in iglob
    if not has_magic(pathname):

The contents of 'test.csv':

Test,File
1,2
3,4
5,6

I'm pretty new to this system, just saw it on the blogpost. Is 2.7 not supported, have I done something fundamentally wrong, or have I found a bug? I pretty much can't get anything to work beyond simple built-in types:

>>> into(list, {1,2,3,4,5})
[1, 2, 3, 4, 5]

putting this issue here, as it's an into conversion, not really a blaze specific thing

blaze/blaze#763

The Chunks type is really just anything over which we can iterate. It doesn't actually have to be used in a chunking workflow. It might be best to rename it appropriately.

Separately, we might want to make an IndexableOf type that supports both iteration and also indexing. Whereas IterableOf(a)takes an iterable or a function f :: -> [a], IndexableOf(a) might take either an indexable (like a list) or a function f :: int -> a and a number of elements. IndexableOf might also be extended to something n-dimensional, so possibly f :: [int] -> a and a shape rather than a number of elements.

SparkSQL officially speaks Hive. I think we're currently sending it MySQL and possibly through the wrong method. If we have pyhive install we should probably use that instead.

It would be nice to automate the metadata loading process from CSV files on HDFS into the Hive metastore. In principle the CREATE TABLE statement can be handled as we already do in backends/sql_csv.py. Some novel parts

1. A new HDFS metaclass (similar to Chunks)
2. Need to download a csv file from HDFS to the local machine so that we can discover its datashape
3. Ideally we only pull a few hundred kilobyte sample, not the whole thing.

The metaclass can probably be a no-op.

PyWebHDFS or more generally the WebHDFS web interface seems like a reasonable approach to interacting with a foreign HDFS. I'm running into issues with it now though; it seems that some link gets internally redirected and becomes opaque.

We may want to handle the downloading/HTTP request ourselves so that we can use an HTTP RANGE GET request (or anything really that lets us download just some of a file.)

cc @quasiben

This is sort of a gateway issue to handling HDFS data generally. HDFS(CSV) -> pyspark.RDD could be a thing as well as (if we find a suitable library, HDFS(CSV) -> HDFS(Parquet).

There doesn't seem to be a way to get from an HDFStore to SQL, though you can go from an HDFStore to a Dataframe and a DataFrame to SQL.

This fails:

blz.into('sqlite:///bayarea.db::buildings', 'hdfstore://bayarea.h5::/buildings')

with this error:

NetworkXNoPath: node <class 'pandas.io.pytables.FrameFixed'> not reachable from <class '_abcoll.Iterator'>

But this works fine:

blz.into(pd.DataFrame, 'hdfstore://bayarea.h5::/buildings')

And this works fine:

buildings = store['buildings']
blz.into('sqlite:///bayarea.db::buildings', buildings)

blaze issue migration

blaze/blaze#690

original error from @mrocklin

In [1]: !cat data.csv
Name,Daily
A,0.15
B,0.135
C,0.110

In [2]: from blaze import *

In [3]: csv = CSV('data.csv')

In [4]: Table(csv)
Out[4]: 
  Name  Daily
0    A  0.150
1    B  0.135
2    C  0.110

In [5]: sql = SQL('sqlite:///:memory:', 'data', schema=csv.schema)

In [6]: into(sql, csv)
Out[6]: <blaze.data.sql.SQL at 0x7f4a5a82b410>

In [7]: list(sql)
ValueError: parse error converting string "Daily" to float64

cc @cpcloud @quasiben

The newly add ( #26 ) JSON endpoints don't yet support reading/writing gzip files. In theory this isn't hard to add but in practice making this work on Windows/Mac/Linux for Python 2/3 can be a bit of a pain.

Help welcome.

Sometimes we might want to do a computation and then keep the result in the database as another table. To do this Blaze needs an append(sqlalchemy.Table, sqlalchemy.Select) implementation. Presumably there is some way to generate an approprate INSERT INTO ... statement through sqlalchemy.

We lack direct routes to copy data between remote computers without touching the local system.

into('ssh://A:myfile.csv', 'ssh://A:myfile2.csv')  # Should be a remote local copy
into('ssh://A:myfile.csv', 'ssh://B:myfile.csv')  # Should result in a remote call to scp
into('hdfs://A:myfile.csv', 'ssh://A.myfile.csv')  # Should result in hadoop fs calls
into('ssh://A:myfile.csv', 'hdfs://A.myfile.csv')  # Should result in hadoop fs calls
...

When interacting with AWS the inter-node bandwidth is very high. It'd be nice to leverage this instead of pulling down and pushing up from local.

it seems that a call to conn.get_bucket() can't succeed in a reasonable amount of time

#35 (comment)

Sometimes fields in tables were created with the AUTOINCREMENT command.
into should ignore this column and (let sqlalchemy) take care of this field when a user inserts data that does not have this field defined.

import os
import sqlite3
import pandas as pd
import numpy as np
from blaze import *

DB = 'test.db'
TABLE = 'position'

try:
    os.remove(DB)
except OSError:
    pass

conn = sqlite3.connect(DB)
conn.execute(
    '''CREATE TABLE {} (
    id integer NOT NULL PRIMARY KEY AUTOINCREMENT,
    x real NOT NULL,
    y real NOT NULL,
    z real NOT NULL
    )'''.format(TABLE)
    )
conn.close()

positions = pd.DataFrame(np.random.randn(10, 3), columns=['x', 'y', 'z'])

table_url = 'sqlite:///' + DB + '::' + TABLE

into(table_url, positions)

blaze 0.7.2, into 0.2.2

so 2 issues

• I think [14] should work (at least according to the spec)
• I think [12] should work as well, so maybe do some splitting on a the os.path.sep
  and then rejoin the paths?

Valid SQLite URL forms are:
 sqlite:///:memory: (or, sqlite://)
 sqlite:///relative/path/to/file.db
 sqlite:////absolute/path/to/file.db

In [1]: from blaze import Data

In [2]: import sqlite3

In [3]: db = sqlite3.connect('foo.db')

In [6]: import pandas as pd

In [8]: import numpy as np

In [9]: pd.DataFrame(np.random.rand(5,5)).to_sql('example',db)

In [10]: Data('sqlite:///foo.db')
Out[10]:
Data:       Engine(sqlite:///foo.db)
DataShape:  {
  example: var * {
    index: ?int32,
    0: ?float64,
    1: ?float64,
    2: ?float64,
    3: ?float64,
  ...

In [11]: Data('sqlite:///C:foo.db')
Out[11]:
Data:       Engine(sqlite:///C:foo.db)
DataShape:  {
  example: var * {
    index: ?int32,
    0: ?float64,
    1: ?float64,
    2: ?float64,
    3: ?float64,
  ...

# ok as the paths aren't escaped propertly
In [12]: Data('sqlite:///C:\Users\Jeff Reback\foo.db')
OperationalError: (OperationalError) unable to open database file None None

In [13]: Data('sqlite:///C:\\Users\\Jeff Reback\\foo.db')
Out[13]:
Data:       Engine(sqlite:///C:\Users\Jeff Reback\foo.db)
DataShape:  {
  example: var * {
    index: ?int32,
    0: ?float64,
    1: ?float64,
    2: ?float64,
    3: ?float64,
  ...

# shouldn't this work?
In [14]: Data('sqlite:////C:\\Users\\Jeff Reback\\foo.db')
OperationalError: (OperationalError) unable to open database file None None

When executing the tests in backends/tests, several tests fail, since an exception is thrown when trying to delete temporary files.

py.test test_hdfstore.py

←[1m============================= test session starts =============================←[0m
platform win32 -- Python 2.7.8 -- py-1.4.26 -- pytest-2.6.4
collected 12 items ←[0m←[1m
←[0m
test_hdfstore.py FFFFFFFFFFFF

================================== FAILURES ===================================
________________________________ test_discover ________________________________

←[1m    def test_discover():←[0m
←[1m>       with tmpfile('hdf5') as fn:←[0m
←[1m            df.to_hdf(fn, '/a/b/data')←[0m

test_hdfstore.py:39:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
..\..\..\..\contextlib.py:17: in __enter__
←[1m    return self.gen.next()←[0m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

extension = '.hdf5'

←[1m    @contextmanager←[0m
←[1m    def tmpfile(extension=''):←[0m
←[1m        extension = '.' + extension.lstrip('.')←[0m
←[1m        handle, filename = tempfile.mkstemp(extension)←[0m
←[1m>       os.remove(filename)←[0m
←[1m←[31mE       WindowsError: [Error 32] Der Prozess kann nicht auf die Datei zugreifen, da sie von einem anderen Prozess verwendet wird: 'c:\\users\\acbdef\\appdata\\local\\temp\\tmpq9_zuc.hdf5'←[0m

..\..\utils.py:44: WindowsError

Content of json file located in user-temp directory:

[{"amount": 100, "name": "Alice"}, {"amount": 200, "name": "Bob"}]

$ py.test test_json.py
←[1m============================= test session starts =============================←[0m
platform win32 -- Python 2.7.8 -- py-1.4.26 -- pytest-2.6.4
collected 21 items ←[0m←[1m
←[0m
test_json.py ...FF........F....F..

================================== FAILURES ===================================
________________________________ test_resource ________________________________

←[1m    def test_resource():←[0m
←[1m>       assert isinstance(resource('jsonlines://foo.json'), JSONLines)←[0m
←[1m←[31mE       assert isinstance(<into.directory._Directory object at 0x0422CEB0>, JSONLines)←[0m
←[1m←[31mE        +  where <into.directory._Directory object at 0x0422CEB0> = resource('jsonlines://foo.json')←[0m

test_json.py:55: AssertionError
___________________________ test_resource_guessing ____________________________

←[1m    def test_resource_guessing():←[0m
←[1m        with json_file(dat) as fn:←[0m
←[1m>           assert isinstance(resource(fn), JSON)←[0m

test_json.py:63:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
..\..\regex.py:64: in __call__
←[1m    return self.dispatch(s)(s, *args, **kwargs)←[0m
..\..\directory.py:57: in resource_directory
←[1m    subtype = type(resource(one_uri, **kwargs))←[0m
..\..\regex.py:64: in __call__
←[1m    return self.dispatch(s)(s, *args, **kwargs)←[0m
!!! Recursion detected (same locals & position)
___________________________ test_multiple_jsonlines ___________________________

←[1m    def test_multiple_jsonlines():←[0m
←[1m        try:←[0m
←[1m            with open('_test_a1.json', 'w') as f:←[0m
←[1m                json.dump(dat, f)←[0m
←[1m            with open('_test_a2.json', 'w') as f:←[0m
←[1m                json.dump(dat, f)←[0m
←[1m>           r = resource('_test_a*.json')←[0m

test_json.py:154:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
..\..\regex.py:64: in __call__
←[1m    return self.dispatch(s)(s, *args, **kwargs)←[0m
..\..\directory.py:57: in resource_directory
←[1m    subtype = type(resource(one_uri, **kwargs))←[0m
..\..\regex.py:64: in __call__
←[1m    return self.dispatch(s)(s, *args, **kwargs)←[0m
..\..\directory.py:57: in resource_directory
←[1m    subtype = type(resource(one_uri, **kwargs))←[0m
..\..\regex.py:64: in __call__
←[1m    return self.dispatch(s)(s, *args, **kwargs)←[0m
!!! Recursion detected (same locals & position)
_____________________________ test_resource_gzip ______________________________

←[1m    def test_resource_gzip():←[0m
←[1m>       assert isinstance(resource('foo.json.gz'), (JSON, JSONLines))←[0m
←[1m←[31mE       assert isinstance(<into.directory._Directory object at 0x04364030>, (<class 'into.backends.json.JSON'>, <cla
ss 'into.backends.json.JSONLines'>))←[0m
←[1m←[31mE        +  where <into.directory._Directory object at 0x04364030> = resource('foo.json.gz')←[0m

test_json.py:206: AssertionError
←[1m←[31m===================== 4 failed, 17 passed in 2.78 seconds =====================←[0m```

Sorry for opening a new ticket, but I'm not sure how to reopen the old one ...

Most of the tests are passed on my Windows, Python 2.7 computer, except for:

Connection forbidden: Most probably related to a firewall on my computer ...

_______________________ ERROR collecting test_mongo.py ________________________
test_mongo.py:15: in <module>
←[1m    conn = pymongo.MongoClient()←[0m
..\..\..\pymongo\mongo_client.py:377: in __init__
←[1m    raise ConnectionFailure(str(e))←[0m
←[1m←[31mE   ConnectionFailure: [Errno 10061] Es konnte keine Verbindung hergestellt werden, da der Zielcomputer die Verbindung verweigerte←[0m

Test file missing .. (I used pip install to install package to my local python installation). Test is passed when airline.sas7bdat is available in the test directory.

________________________ ERROR collecting test_sas.py _________________________
test_sas.py:18: in <module>
←[1m    sasfile = SAS7BDAT(test_path)←[0m
..\..\..\sas7bdat.py:422: in __init__
←[1m    self._file = open(self.path, 'rb')←[0m
←[1m←[31mE   IOError: [Errno 2] No such file or directory: 'c:\\Python27\\Lib\\site-packages\\into\\backends\\tests\\airline.
sas7bdat'←[0m

'ssh' is not defined in 'except socket.error:'

________________________ ERROR collecting test_ssh.py _________________________
test_ssh.py:25: in <module>
←[1m    ssh.close()←[0m
←[1m←[31mE   NameError: name 'ssh' is not defined←[0m
←[1m←[31m========== 137 passed, 5 skipped, 2 xfailed, 3 error in 7.10 seconds ==========←[0m

ipdb> convert(pd.Series, set([1,2,3]))
*** AttributeError: 'NoneType' object has no attribute 'measure'

this is because discover isn't defined on sets

Maybe I'm missing something, but why does Odo detect and reroute on NotImplementedError instead of NotImplemented?

Python docs advise raising NotImplementError from abstract methods that are supposed to be overwritten by derived classes.

NotImplemented is intended for "rich comparisons" and choosing which object should handle these comparisons.

Odo's path choosing seems to be more analogous to the latter rather than the former. So maybe backends should return NotImplemented instead of raising NotImplementedError. This might benefit performance slightly too, since raising and catching exceptions probably incurs more overhead than returning and checking constants.

The into command #69 provides a command line interface to the into function

into myfile.csv myfile.json --delimter ;

Here is a list of possible improvements

• Decrease into import times and latency
• Support keywords anywhere, e.g. into myfile.csv --delimiter ; myfile.json
• Some way to discover keywords based on the file type man into, into csv --help, etc...
• Cleaner error reporting than the current Python stack traces
• Progress bar or at least some reassuring token that promises that progress is being made

@cpcloud and @quasiben came up with most of these ideas. Probably I'm forgetting a few. Please treat this comment as wiki space and add appropriately.

From a private conversation:

Would be nice if into could move files in S3 to e.g., Redshift

data = into('redshift://conn_info::table', 's3://my/s3/bucket/*.csv')

A common use of into would be to import/convert all of the csv files in a directory. This works fine if there is a prefix to the asterix, e.g.,
into(pd.DataFrame, 'examp*.csv')

but errors if there is no prefix,

>>> into(pd.DataFrame, '*.csv')
...
IOError: File *.csv does not exist

e.g.

this is an append
odo(...,'hdfstore://foo.h5::bar')

and this is not
odo(...,'hdfstore://foo.h5::bar',mode='w')

For operations on files with unknown extensions like the following

$ into s3://mybucket/myfile.xyz  myfile.xyz

We currently fail because we can't assign a particular node based off of the extension. One solution would be to create a Blob type to handle unknown things like this.

This breaks both

pip install into
  The behavior of the `>` version specifier has changed in PEP 440. `>` is now an exclusive operator
, meaning that >0.15 does not match >0.15.*. Perhaps you want `>=` instead of `>`?
  Could not find a version that satisfies the requirement pandas>0.15 (from into) (from versions: 0.
1, 0.2b0, 0.2b1, 0.2, 0.3.0b0, 0.3.0b2, 0.3.0, 0.4.0, 0.4.1, 0.4.2, 0.4.3, 0.5.0, 0.6.0, 0.6.1, 0.7.
0rc1, 0.7.0, 0.7.1, 0.7.2, 0.7.3, 0.8.0rc1, 0.8.0rc2, 0.8.0, 0.8.1, 0.9.0, 0.9.1, 0.10.0, 0.10.1, 0.
11.0, 0.12.0, 0.13.0, 0.13.1, 0.14.0, 0.14.1, 0.15.0, 0.15.1, 0.15.2)
  No distributions matching the version for pandas>0.15 (from into)

python setup.py install
Searching for pandas>0.15
Reading https://pypi.python.org/simple/pandas/
No local packages or download links found for pandas>0.15
error: Could not find suitable distribution for Requirement.parse('pandas>0.15')

(Tested under windows 7, python3.4 x64 with latest pip, setuptools)

Apparently culprit is commit e44a1ec

Blaze still holds our recipes for PySpark and SparkSQL in the old "just into" style. These should be brought over to into and translated into append/convert style.

It's entirely cosmetic, but the graphviz network should be updated with odo -- perhaps there is some new functionality which could be merged into the network?

Following this notebook I thought it would be nice to test with my data in a bcolz file. Unfortunately, I ran into a NotImplementedError

In [1]: from into import into              

In [2]: into('data.bcolz', 'receivers.csv')
---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
<ipython-input-2-e781d0c64487> in <module>()
----> 1 into('data.bcolz', 'receivers.csv')

/home/rifr/anaconda3/envs/sonair/lib/python3.4/site-packages/multipledispatch/dispatcher.py in __call__(self, *args, **kwargs)
    161             self._cache[types] = func
    162         try:
--> 163             return func(*args, **kwargs)
    164 
    165         except MDNotImplementedError:

/home/rifr/anaconda3/envs/sonair/lib/python3.4/site-packages/into-0.2.1-py3.4.egg/into/into.py in into_string_string(a, b, **kwargs)
    118 def into_string_string(a, b, **kwargs):
    119     r = resource(b, **kwargs)
--> 120     return into(a, r, **kwargs)
    121 
    122 

/home/rifr/anaconda3/envs/sonair/lib/python3.4/site-packages/multipledispatch/dispatcher.py in __call__(self, *args, **kwargs)
    161             self._cache[types] = func
    162         try:
--> 163             return func(*args, **kwargs)
    164 
    165         except MDNotImplementedError:

/home/rifr/anaconda3/envs/sonair/lib/python3.4/site-packages/into-0.2.1-py3.4.egg/into/into.py in into_string(uri, b, **kwargs)
    111         resource_ds = ds
    112 
--> 113     a = resource(uri, dshape=resource_ds, expected_dshape=ds, **kwargs)
    114     return into(a, b, dshape=ds, **kwargs)
    115 

/home/rifr/anaconda3/envs/sonair/lib/python3.4/site-packages/into-0.2.1-py3.4.egg/into/regex.py in __call__(self, s, *args, **kwargs)
     62 
     63     def __call__(self, s, *args, **kwargs):
---> 64         return self.dispatch(s)(s, *args, **kwargs)
     65 
     66     @property

/home/rifr/anaconda3/envs/sonair/lib/python3.4/site-packages/into-0.2.1-py3.4.egg/into/resource.py in resource_all(uri, *args, **kwargs)
     98     discover
     99     """
--> 100     raise NotImplementedError("Unable to parse uri to data resource: " + uri)
    101 
    102 

NotImplementedError: Unable to parse uri to data resource: data.bcolz

I tested with the following small table.

name,runway,takeoff,datetime_nearest_close
S28,28,TRUE,A
S16,16,TRUE,Q
L14,14,FALSE,I

conda list | grep -E 'into|blaze|bcolz'

bcolz                     v0.7.1_91_g0cebb5a      np19py34_1  
blaze                     0.7.1.post036.gd7cfa0e    np19py34_149  
into                      0.2.1              113_g229f8a7  

I tried also with into from master but no luck.

xref #97

Objects like HDF5 files, SQL databases, Mongo Databases, and dicts of chunks of things might all be convertible. One could also imagine adding a very thin Directory type that expressed a filesystem hierarchy as a nested record type.

A natural common interchange format here might include dicts of either chunks(DataFrame) or chunks(ndarray) depending on the underlying dshape. This would also be a good place for chunks(dynd.nd.array).

Loading the following data from a csv and writing it to a SQLite table

,operation,name,aircraft,datetime_nearest
151,S16,24_002_A320,A320,2014-05-15 07:46:15.100000
154,S16,24_005_A321,A321,2014-05-15 08:08:23.200000

using

d = Data('file.csv')
into('sqlite:///../db.sqlite3::data_event', d)

results in the error below.
I was using the anaconda version

into                      0.1.3                np19py34_0 

---------------------------------------------------------------------------
StatementError                            Traceback (most recent call last)
<ipython-input-25-8bc54b31474a> in <module>()
----> 1 into('sqlite:///../db.sqlite3::data_event', d)

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/multipledispatch/dispatcher.py in __call__(self, *args, **kwargs)
    161             self._cache[types] = func
    162         try:
--> 163             return func(*args, **kwargs)
    164 
    165         except MDNotImplementedError:

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/blaze/interactive.py in into(a, b, **kwargs)
    258     result = compute(b, **kwargs)
    259     kwargs['dshape'] = b.dshape
--> 260     return into(a, result, **kwargs)
    261 
    262 

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/multipledispatch/dispatcher.py in __call__(self, *args, **kwargs)
    161             self._cache[types] = func
    162         try:
--> 163             return func(*args, **kwargs)
    164 
    165         except MDNotImplementedError:

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/into/into.py in into_string(uri, b, **kwargs)
     72         pass
     73     a = resource(uri, **kwargs)
---> 74     return into(a, b, **kwargs)
     75 
     76 

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/multipledispatch/dispatcher.py in __call__(self, *args, **kwargs)
    161             self._cache[types] = func
    162         try:
--> 163             return func(*args, **kwargs)
    164 
    165         except MDNotImplementedError:

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/into/into.py in into_object(a, b, **kwargs)
     58     except NotImplementedError:
     59         pass
---> 60     return append(a, b, **kwargs)
     61 
     62 

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/multipledispatch/dispatcher.py in __call__(self, *args, **kwargs)
    161             self._cache[types] = func
    162         try:
--> 163             return func(*args, **kwargs)
    164 
    165         except MDNotImplementedError:

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/into/backends/sql.py in append_anything_to_sql_Table(t, o, **kwargs)
    234 @append.register(sa.Table, object)
    235 def append_anything_to_sql_Table(t, o, **kwargs):
--> 236     return append(t, convert(Iterator, o, **kwargs), **kwargs)
    237 
    238 

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/multipledispatch/dispatcher.py in __call__(self, *args, **kwargs)
    161             self._cache[types] = func
    162         try:
--> 163             return func(*args, **kwargs)
    164 
    165         except MDNotImplementedError:

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/into/backends/sql.py in append_iterator_to_table(t, rows, **kwargs)
    227     with engine.connect() as conn:
    228         for chunk in partition_all(1000, rows):  # TODO: 1000 is hardcoded
--> 229             conn.execute(t.insert(), chunk)
    230 
    231     return t

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/sqlalchemy/engine/base.py in execute(self, object, *multiparams, **params)
    727                 type(object))
    728         else:
--> 729             return meth(self, multiparams, params)
    730 
    731     def _execute_function(self, func, multiparams, params):

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/sqlalchemy/sql/elements.py in _execute_on_connection(self, connection, multiparams, params)
    320 
    321     def _execute_on_connection(self, connection, multiparams, params):
--> 322         return connection._execute_clauseelement(self, multiparams, params)
    323 
    324     def unique_params(self, *optionaldict, **kwargs):

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/sqlalchemy/engine/base.py in _execute_clauseelement(self, elem, multiparams, params)
    824             compiled_sql,
    825             distilled_params,
--> 826             compiled_sql, distilled_params
    827         )
    828         if self._has_events or self.engine._has_events:

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/sqlalchemy/engine/base.py in _execute_context(self, dialect, constructor, statement, parameters, *args)
    891             self._handle_dbapi_exception(e,
    892                                          util.text_type(statement), parameters,
--> 893                                          None, None)
    894 
    895         if context.compiled:

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/sqlalchemy/engine/base.py in _handle_dbapi_exception(self, e, statement, parameters, cursor, context)
   1157                 util.raise_from_cause(
   1158                     sqlalchemy_exception,
-> 1159                     exc_info
   1160                 )
   1161             else:

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/sqlalchemy/util/compat.py in raise_from_cause(exception, exc_info)
    186             exc_info = sys.exc_info()
    187         exc_type, exc_value, exc_tb = exc_info
--> 188         reraise(type(exception), exception, tb=exc_tb, cause=exc_value)
    189 else:
    190     exec("def reraise(tp, value, tb=None, cause=None):\n"

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/sqlalchemy/util/compat.py in reraise(tp, value, tb, cause)
    179             value.__cause__ = cause
    180         if value.__traceback__ is not tb:
--> 181             raise value.with_traceback(tb)
    182         raise value
    183 

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/sqlalchemy/engine/base.py in _execute_context(self, dialect, constructor, statement, parameters, *args)
    887                 conn = self._revalidate_connection()
    888 
--> 889             context = constructor(dialect, self, conn, *args)
    890         except Exception as e:
    891             self._handle_dbapi_exception(e,

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/sqlalchemy/engine/default.py in _init_compiled(cls, dialect, connection, dbapi_connection, compiled, parameters)
    571                 for key in self.compiled.positiontup:
    572                     if key in processors:
--> 573                         param.append(processors[key](compiled_params[key]))
    574                     else:
    575                         param.append(compiled_params[key])

/home/rifr/anaconda3/envs/prj/lib/python3.4/site-packages/sqlalchemy/dialects/sqlite/base.py in process(value)
    370                 }
    371             else:
--> 372                 raise TypeError("SQLite DateTime type only accepts Python "
    373                                 "datetime and date objects as input.")
    374         return process

StatementError: SQLite DateTime type only accepts Python datetime and date objects as input. (original cause: TypeError: SQLite DateTime type only accepts Python datetime and date objects as input.) 'INSERT INTO data_event (name, operation_id, datetime_nearest, aircraft) VALUES (?, ?, ?, ?)' ({'name': 151, 'aircraft': 'A320', 'datetime_nearest': '24_002_A320', 'operation_id': 'S16'}, {'name': 154, 'aircraft': 'A321', 'datetime_nearest': '24_005_A321', 'operation_id': 'S16'})

On the SQLAlchemy website it is stated that while SQLite doesn't support Datetime's natively, SQLAlchemy can take care of this, so I would expect into to work just fine with datetimes.

In [28]: d.dshape
Out[28]: 
dshape("""var * {
  Unnamed: 0: int64,
  operation: string,
  name: string,
  aircraft: string,
  datetime_nearest: datetime
  }""")

I'd be ok with not having it for py26

might be interesting to provide

odo(df, rpy2.robjects.vectors.DataFrame)

This is being deprecated in pandas 0.16.0, see here to be handled directly by rpy2

Current pandas docs are here

@mrocklin @jreback

I would like to augment the blaze server so that if someone updates an hdfstore, the blaze server can do compute on the updated file.

Here is my plan, I'd like your feedback on it

• subclass hdfstore such that if timestamps change, the pytables file handle will be reopened if it's stale
• add an entry to resource to return this subclass

(maybe we call it livehdfstore?)

The other option is to look for a kwarg in the resource call (live=True?) and dispatch based on that to either the original hdfstore, or the subclass?

In 0.2.1, importing multiple csv's, at least one of which has null values, results in errors.

One csv has null vaules in a column and another doesn't

Error:
Exception: Integer column has NA values

Example:

examp1.csv:

a,b,c
1,2,3
4,5,6
7,8,9

examp2.csv:

a,b,c
1,2,3
4,,6
7,8,9

examp.py

into(pd.DataFrame, 'examp*.csv')

Multiple csv's have null values in the same column

Error:
ValueError: cannot convert float NaN to integer

Example:

examp1.csv:

a,b,c
1,2,3
4,,6
7,8,9

examp2.csv:

a,b,c
1,,2
3,4,5
6,7,8

examp.py

into(pd.DataFrame, 'examp*.csv')

In both of these cases importing the csv's one at a time works fine.

It would be nice to quickly view the contents of an S3 bucket

>>> discover(resource('s3://blaze_data'))
...

They're currently hosted at http://into.readthedocs.org/en/latest/ but we can change the domain.

This should be possible with some CNAME magic in the right place.

Converting from SQL (SQLite) to a recarray results in a normal array. Converting from CSV to recarray works fine.

In [9]: rcv = into(np.recarray, FILE_RECEIVERS)
In [10]: type(rcv)
Out[10]: numpy.core.records.recarray
In [11]: rcv = into(np.recarray, TABLE_RECEIVERS)
In [12]: type(rcv)
Out[12]: numpy.ndarray

Could this be due to cost=0.0 for np.array to np.recarray?
Converting first into DataFrame and then into recarray works.

@mrocklin, I was trying to get a new version for python 2.7 in binstar, as instructed by doing:

conda install -c blaze into -f

and I was still getting my previous error:

    /Users/cdoig/anaconda/envs/topik/lib/python2.7/site-packages/into/convert.pyc in series_to_array(s, dshape, **kwargs)
         66 @convert.register(np.ndarray, pd.Series, cost=0.1)
         67 def series_to_array(s, dshape=None, **kwargs):
    ---> 68     dtype = datashape.to_numpy_dtype(datashape.dshape(dshape))
         69     sdtype = s.dtype
         70     values = s.values

I then checked binstar and wasn't able to find a new package for 2.7. I was just seeing this package info:

depends [u'datashape', u'multipledispatch', u'networkx', u'numpy 1.9*', u'pandas', u'python 3.4*', u'toolz']

I then discovered that binstar was creating the same name for all versions:

586[warning] Distribution osx-64/into-0.2.2-8_g68222bc.tar.bz2 already exists ... removing 
587Exit BINSTAR_BUILD_RESULT=success

and it was removing the previous built package (with different python and numpy versions):
e.g.:
https://binstar.org/blaze/into/builds/85/9#log
https://binstar.org/blaze/into/builds/85/8#log

and that the one that remained was the latest:
https://binstar.org/blaze/into/builds/85/11, with numpy=1.9 and python=3.4

I've opened a PR #112 to add the python version to the string, like @tswicegood does in conda-env:
https://github.com/conda/conda-env/blob/develop/conda.recipe/meta.yaml

But still haven't figured out how to include the numpy version.

When we interact with Python data we use full heuristic mode. This does things like say that discover('1.0') == float64. This is appropriate when we guess the datashape of data in text files like CSV. This isn't appropriate when we grab data from Mongo which is already somewhat typed. In these cases a string is a string and we shouldn't do any guess work.

Perhaps the dual meaning of discover should be made more explicit.

Redshift has an stl_load_errors table that we should take advantage of