feat(core): Add read_json_arrow for fast json records reading. Suppor…

…t additional json export backends.

packages/vaex-core/vaex/__init__.py

@@ -507,6 +507,22 @@ def from_json(path_or_buffer, orient=None, precise_float=False, lines=False, cop
                        copy_index=copy_index)
 
 
+def from_json_arrow(file, read_options=None, parse_options=None):
+    """Create a DataFrame from a JSON file using Apache Arrow.
+
+    This is a much faster alternative to `pandas.read_json(file, lines=True)`.
+    The JSON file is read eagerly, and the resulting DataFrame is stored in memory.
+
+    :param str file: Path to the JSON file.
+    :param read_options: PyArrow JSON read options, see https://arrow.apache.org/docs/python/generated/pyarrow.json.ReadOptions.html
+    :param parse_options: PyArrow JSON parse options, see https://arrow.apache.org/docs/python/generated/pyarrow.json.ParseOptions.html
+    :return: DataFrame
+    """
+    import vaex.json
+    ds = vaex.json.DatasetJSON(file, read_options=read_options, parse_options=parse_options)
+    return vaex.from_dataset(ds)
+
+
 @docsubst
 def from_records(records : List[Dict], array_type="arrow", defaults={}) -> vaex.dataframe.DataFrame:
     '''Create a dataframe from a list of dict.

packages/vaex-core/vaex/column.py

@@ -8,6 +8,7 @@
 import pyarrow as pa
 
 import vaex
+import vaex.encoding
 import vaex.utils
 import vaex.cache
 from .array_types import supported_array_types, supported_arrow_array_types, string_types, is_string_type

packages/vaex-core/vaex/csv.py

@@ -9,7 +9,7 @@
 
 import vaex.dataset
 import vaex.file
-from vaex.dataset import Dataset, DatasetFile
+from vaex.dataset import DatasetFile
 from .itertools import pmap, pwait, buffer, consume, filter_none
 from .multithreading import thread_count_default_io, get_main_io_pool
 
@@ -25,7 +25,7 @@ def file_chunks(file, chunk_size, newline_readahead):
         file_size = file.tell()
         file.seek(0)
         begin_offset = 0
-    
+
         done = False
         while not done:
             # find the next newline boundary
@@ -50,7 +50,7 @@ def reader(file_offset=begin_offset, length=end_offset - begin_offset):
 
 def file_chunks_mmap(file, chunk_size, newline_readahead):
     """Bytes chunks, split by chunk_size bytes, on newline boundaries
-    
+
     Using memory mapping (which avoids a memcpy)
     """
     offset = 0
@@ -67,7 +67,7 @@ def file_chunks_mmap(file, chunk_size, newline_readahead):
 
         file_map = mmap.mmap(file.fileno(), file_size, **kwargs)
         data = memoryview(file_map)
-    
+
         done = False
         while not done:
             # find the next newline boundary
@@ -218,7 +218,7 @@ def close(self):
 
     def _chunk_producer(self, columns, chunk_size=None, reverse=False, start=0, end=None):
         pool = get_main_io_pool()
-        
+
         first = True
         previous = None
         for i, reader in enumerate(file_chunks_mmap(self.path, self.chunk_size, self.newline_readahead)):
@@ -287,7 +287,7 @@ def chunk_reader(reader=reader, first=first, previous=previous, fragment_info=fr
                         # we only need to cut off a piece of the end
                         length = end - row_start
                         table = table.slice(0, length)
-                
+
                 # table = table.combine_chunks()
                 assert len(table)
                 chunks = dict(zip(table.column_names, table.columns))

packages/vaex-core/vaex/dataframe.py

@@ -7027,7 +7027,7 @@ def export_csv_pandas(self, path, progress=None, chunk_size=default_chunk_size,
         return
 
     @docsubst
-    def export_json(self, to, progress=None, chunk_size=default_chunk_size, parallel=True, fs_options=None, fs=None):
+    def export_json(self, to, progress=None, chunk_size=default_chunk_size, parallel=True, fs_options=None, fs=None, backend='pandas', lines=False):
         """ Exports the DataFrame to a CSV file.
 
         :param to: filename or file object
@@ -7036,36 +7036,47 @@ def export_json(self, to, progress=None, chunk_size=default_chunk_size, parallel
         :param parallel: {evaluate_parallel}
         :param fs_options: {fs_options}
         :param fs: {fs}
+        :param backend: Which backend to use for writting the JSON file. Can be "pandas" or "json".
+        :param lines: If True, each row is written as a separate JSON record. If False, dataframe is written as a list of JSON records.
         :return:
         """
+
         json = None  # we may want to pass the module as parameter to use a faster library
         import json as json_std
         json = json or json_std
 
-        # not sure if we want to use pandas, it will treat datetime for us, but will convert null to nan
-        use_pandas = True
-
         # we take on the '[' and ']' from each chunk, and insert it back ourselves
         # and we also need to but ',' between each chunk
         with vaex.progress.tree(progress, title="export(json)"), vaex.file.open(path=to, mode='wb', fs_options=fs_options, fs=fs) as f:
-            f.write(b"[")
+            if not lines:
+                f.write(b"[")
             first = True
-            if use_pandas:
+            if backend == 'pandas':
                 for _i1, _i2, df in self.to_pandas_df(chunk_size=chunk_size, parallel=parallel):
-                    if not first:
+                    if (not first) and (not lines):
                         f.write(b", ")
                     first = False
                     f_temp = io.StringIO()
-                    df.to_json(f_temp, orient='records')
-                    f.write(f_temp.getvalue()[1:-1].encode('utf8'))
-            else:
+                    df.to_json(f_temp, orient='records', lines=lines)
+                    if lines:
+                        f.write(f_temp.getvalue().encode('utf8'))
+                    else:
+                        f.write(f_temp.getvalue()[1:-1].encode('utf8'))
+            elif backend == 'json':
                 for _i1, _i2, records in self.to_records(chunk_size=chunk_size, parallel=parallel):
-                    if not first:
+                    if (not first) and (not lines):
                         f.write(b", ")
                     first = False
                     raw = json.dumps(records)[1:-1]
+                    if (not first) and (lines):
+                        raw = raw.replace('},', '}\n')
                     f.write(raw.encode("utf8"))
-            f.write(b"]")
+                    f.write('\n'.encode('utf8'))
+            else:
+                raise ValueError(f"Unknown backend {backend}, should be 'pandas' or 'json'.")
+            if not lines:
+                f.write(b"]")
+
 
     def _needs_copy(self, column_name):
         import vaex.file.other

packages/vaex-core/vaex/json.py

@@ -4,8 +4,11 @@
 import numpy as np
 import pyarrow as pa
 
+from frozendict import frozendict
+
 import vaex
-from vaex.encoding import Encoding
+import vaex.dataset
+import vaex.encoding
 
 
 serializers = []
@@ -208,9 +211,58 @@ def default(self, obj):
 class VaexJsonDecoder(json.JSONDecoder):
     def __init__(self, *args, **kwargs):
         json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
-    
+
     def object_hook(self, dct):
         for serializer in serializers:
             if serializer.can_decode(dct):
                 return serializer.decode(dct)
         return dct
+
+
+@vaex.dataset.register
+class DatasetJSON(vaex.dataset.DatasetFile):
+    snake_name = "arrow-json"
+
+    def __init__(self, path, read_options=None, parse_options=None, fs=None, fs_options={}):
+        super(DatasetJSON, self).__init__(path, fs=fs, fs_options=fs_options)
+        self.read_options = read_options
+        self.parse_options = parse_options
+        self._read_file()
+
+    @property
+    def _fingerprint(self):
+        fp = vaex.file.fingerprint(self.path, fs_options=self.fs_options, fs=self.fs)
+        return f"dataset-{self.snake_name}-{fp}"
+
+    def _read_file(self):
+        import pyarrow.json
+
+        with vaex.file.open(self.path, fs=self.fs, fs_options=self.fs_options, for_arrow=True) as f:
+            try:
+                codec = pa.Codec.detect(self.path)
+            except Exception:
+                codec = None
+            if codec:
+                f = pa.CompressedInputStream(f, codec.name)
+            self._arrow_table = pyarrow.json.read_json(f, read_options=self.read_options, parse_options=self.parse_options)
+        self._columns = dict(zip(self._arrow_table.schema.names, self._arrow_table.columns))
+        self._set_row_count()
+        self._ids = frozendict({name: vaex.cache.fingerprint(self._fingerprint, name) for name in self._columns})
+
+    def _encode(self, encoding):
+        spec = super()._encode(encoding)
+        del spec["write"]
+        return spec
+
+    def __getstate__(self):
+        state = super().__getstate__()
+        state["read_options"] = self.read_options
+        state["parse_options"] = self.parse_options
+        return state
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        self._read_file()
+
+    def close(self):
+        pass

packages/vaex-core/vaex/utils.py

@@ -1,12 +1,10 @@
 # -*- coding: utf-8 -*-
 from __future__ import absolute_import
 import ast
-import collections
 import concurrent.futures
 import contextlib
 import functools
 import json
-import math
 import os
 import platform
 import re
@@ -27,7 +25,6 @@
 import six
 import yaml
 
-from .json import VaexJsonEncoder, VaexJsonDecoder
 import vaex.file
 
 
@@ -296,14 +293,15 @@ def yaml_load(f):
 
 
 def write_json_or_yaml(file, data, fs_options={}, fs=None, old_style=True):
+    import vaex.json
     file, path = vaex.file.file_and_path(file, mode='w', fs_options=fs_options, fs=fs)
     try:
         if path:
             base, ext = os.path.splitext(path)
         else:
             ext = '.json'  # default
         if ext == ".json":
-            json.dump(data, file, indent=2, cls=VaexJsonEncoder if old_style else None)
+            json.dump(data, file, indent=2, cls=vaex.json.VaexJsonEncoder if old_style else None)
         elif ext == ".yaml":
             yaml_dump(file, data)
         else:
@@ -313,14 +311,15 @@ def write_json_or_yaml(file, data, fs_options={}, fs=None, old_style=True):
 
 
 def read_json_or_yaml(file, fs_options={}, fs=None, old_style=True):
+    import vaex.json
     file, path = vaex.file.file_and_path(file, fs_options=fs_options, fs=fs)
     try:
         if path:
             base, ext = os.path.splitext(path)
         else:
             ext = '.json'  # default
         if ext == ".json":
-            return json.load(file, cls=VaexJsonDecoder if old_style else None) or {}
+            return json.load(file, cls=vaex.json.VaexJsonDecoder if old_style else None) or {}
         elif ext == ".yaml":
             return yaml_load(file) or {}
         else:

tests/from_json_test.py

@@ -1,6 +1,8 @@
 from common import *
 import tempfile
 
+import vaex
+
 
 def test_from_json(ds_local):
     df = ds_local
@@ -18,3 +20,30 @@ def test_from_json(ds_local):
     assert tmp_df.x.tolist() == df.x.tolist()
     assert tmp_df.bool.tolist() == df.bool.tolist()
 
+
+@pytest.mark.parametrize("backend", ["pandas", "json"])
+@pytest.mark.parametrize("lines", [False, True])
+def test_from_and_export_json(tmpdir, ds_local, backend, lines):
+    df = ds_local
+    df = df.drop(columns=['datetime'])
+    if 'timedelta' in df:
+        df = df.drop(columns=['timedelta'])
+    if 'obj' in df:
+        df = df.drop(columns=['obj'])
+
+    # Create temporary json files
+    tmp = str(tmpdir.join('test.json'))
+    df.export_json(tmp, backend=backend, lines=lines)
+
+    # Check if file can be read with default (pandas) backend
+    df_read = vaex.from_json(tmp, lines=lines)
+    assert df.shape == df_read.shape
+    assert df.x.tolist() == df_read.x.tolist()
+    assert df.get_column_names() == df_read.get_column_names()
+
+    # If lines is True, check if the file can be read with the from_json_arrow function
+    if lines:
+        df_read_arrow = vaex.from_json_arrow(tmp)
+        assert df.shape == df_read_arrow.shape
+        assert df.x.tolist() == df_read_arrow.x.tolist()
+        assert df.get_column_names() == df_read_arrow.get_column_names()