adds ability to set field types explicitly

.gitignore

@@ -0,0 +1 @@
+.vscode

README.md

@@ -26,7 +26,8 @@ bulk_insert.py GRAPHNAME [OPTIONS]
 |  -t     | --max-token-count INT |    max number of tokens sent in each Redis query (default 1024) |
 |  -b     | --max-buffer-size INT |    max batch size (MBs) of each Redis query (default 4096)      |
 |  -c     | --max-token-size INT  |    max size (MBs) of each token sent to Redis (default 500)     |
-|  -q     | --quote-minimal       |    enable smart quoting for items within the CSV                |
+|  -q     | --quote               |    the quoting format used in the CSV file. QUOTE_MINIMAL=0,QUOTE_ALL=1,QUOTE_NONNUMERIC=2,QUOTE_NONE=3 |
+|  -f     | --field-types         |    json to set explicit types for each field, format {<label>:[<col1 type>, <col2 type> ...]} where type can be 0(null),1(bool),2(numeric),3(string)       |
 
 
 The only required arguments are the name to give the newly-created graph (which can appear anywhere) and at least one node CSV file.
@@ -39,6 +40,15 @@ python bulk_insert.py GRAPH_DEMO -n example/Person.csv -n example/Country.csv -r
 ```
 The label (for nodes) or relationship type (for relationships) is derived from the base name of the input CSV file. In this example, we'll construct two sets of nodes, labeled `Person` and `Country`, and two types of relationships - `KNOWS` and `VISITED`.
 
+The default behaviour is to infer the type for each row based on the value of each row, which can cause type mismatch problem. For example if a string property contains string values of 'false', 'true' or numbers. To avoid this, use --field-types to explicitly set the type for each column in the csv. 
+EG, to explicitly set to string.
+
+```
+python3 bulk_insert.py ROBOTS -f '{"Robots" : [3]}' -q1 -n example2/Robots.csv 
+```
+
+Notice that when -f isn't used, the robot name "30165" would be inserted as a number rather than a string which causes problems in RedisGraph when searching. 
+
 ## Input constraints
 ### Node identifiers
 - If both nodes and relations are being created, each node must be associated with a unique identifier.
@@ -55,6 +65,8 @@ The label (for nodes) or relationship type (for relationships) is derived from t
     - `numeric`: an unquoted value that can be read as a floating-point or integer type.
     - `string`: any field that is either quote-interpolated or cannot be casted to a numeric or boolean type.
     - `NULL`: an empty field.
+- Default behaviour is to infer the property type, attempting to cast it to null, float, boolean or string in that order. 
+- If explicit type is required, for example, if a value is "1234" and it must not be inferred into a float, you can use the option -f to specify the type explicitly for each row being imported. 
 
 ### Label file format:
 - Each row must have the same number of fields.
@@ -68,3 +80,4 @@ The label (for nodes) or relationship type (for relationships) is derived from t
 - The first two fields of each row are the source and destination node identifiers. The names of these fields in the header do not matter.
 - If the file has more than 2 fields, all subsequent fields are relationship properties that adhere to the same rules as node properties.
 - Described relationships are always considered to be directed (source->destination).
+

bulk_insert.py

@@ -6,13 +6,16 @@
 from timeit import default_timer as timer
 import redis
 import click
+import json
 
 # Global variables
 CONFIGS = None         # thresholds for batching Redis queries
 NODE_DICT = {}         # global node dictionary
 TOP_NODE_ID = 0        # next ID to assign to a node
 QUERY_BUF = None       # Buffer for query being constructed
 
+FIELD_TYPES = None
+
 # Custom error class for invalid inputs
 class CSVError(Exception):
     pass
@@ -155,8 +158,13 @@ def pack_header(self, header):
     # Convert a list of properties into a binary string
     def pack_props(self, line):
         props = []
-        for field in line[self.prop_offset:]:
-            props.append(prop_to_binary(field))
+        for num, field in enumerate(line[self.prop_offset:]):
+            try :
+                FIELD_TYPES[self.entity_str][num]
+            except :
+                props.append(prop_to_binary(field, None))
+            else :
+                props.append(prop_to_binary(field, FIELD_TYPES[self.entity_str][num]))
 
         return b''.join(p for p in props)
 
@@ -278,31 +286,39 @@ def process_entities(self, expected_col_count):
 
 # Convert a single CSV property field into a binary stream.
 # Supported property types are string, numeric, boolean, and NULL.
-def prop_to_binary(prop_str):
+# type is either Type.NUMERIC, Type.BOOL or Type.STRING, and explicitly sets the value to this type if possible
+def prop_to_binary(prop_str, type):
     # All format strings start with an unsigned char to represent our Type enum
     format_str = "=B"
     if not prop_str:
         # An empty field indicates a NULL property
         return struct.pack(format_str, Type.NULL)
 
     # If field can be cast to a float, allow it
-    try:
-        numeric_prop = float(prop_str)
-        return struct.pack(format_str + "d", Type.NUMERIC, numeric_prop)
-    except:
-        pass
-
-    # If field is 'false' or 'true', it is a boolean
-    if prop_str.lower() == 'false':
-        return struct.pack(format_str + '?', Type.BOOL, False)
-    elif prop_str.lower() == 'true':
-        return struct.pack(format_str + '?', Type.BOOL, True)
-
-    # If we've reached this point, the property is a string
-    encoded_str = str.encode(prop_str) # struct.pack requires bytes objects as arguments
-    # Encoding len+1 adds a null terminator to the string
-    format_str += "%ds" % (len(encoded_str) + 1)
-    return struct.pack(format_str, Type.STRING, encoded_str)
+
+    if type == None or type == Type.NUMERIC:
+        try:
+            numeric_prop = float(prop_str)
+            return struct.pack(format_str + "d", Type.NUMERIC, numeric_prop)
+        except:
+            pass
+
+    if type == None or type == Type.BOOL:
+        # If field is 'false' or 'true', it is a boolean
+        if prop_str.lower() == 'false':
+            return struct.pack(format_str + '?', Type.BOOL, False)
+        elif prop_str.lower() == 'true':
+            return struct.pack(format_str + '?', Type.BOOL, True)
+
+    if type == None or type == Type.STRING:
+        # If we've reached this point, the property is a string
+        encoded_str = str.encode(prop_str) # struct.pack requires bytes objects as arguments
+        # Encoding len+1 adds a null terminator to the string
+        format_str += "%ds" % (len(encoded_str) + 1)
+        return struct.pack(format_str, Type.STRING, encoded_str)
+
+    ## if it hasn't returned by this point, it is trying to set it to a type that it can't adopt
+    raise Exception("unable to parse [" + prop_str + "] with type ["+repr(type)+"]")
 
 # For each node input file, validate contents and convert to binary format.
 # If any buffer limits have been reached, flush all enqueued inserts to Redis.
@@ -336,25 +352,30 @@ def process_entity_csvs(cls, csvs, separator):
 @click.option('--max-token-count', '-c', default=1024, help='max number of processed CSVs to send per query (default 1024)')
 @click.option('--max-buffer-size', '-b', default=2048, help='max buffer size in megabytes (default 2048)')
 @click.option('--max-token-size', '-t', default=500, help='max size of each token in megabytes (default 500, max 512)')
-@click.option('--quote-minimal/--no-quote-minimal', '-q/-d', default=False, help='only quote those fields which contain special characters such as delimiter, quotechar or any of the characters in lineterminator')
+@click.option('--quote', '-q', default=3, help='the quoting format used in the CSV file. QUOTE_MINIMAL=0,QUOTE_ALL=1,QUOTE_NONNUMERIC=2,QUOTE_NONE=3')
+@click.option('--field-types', '-f', default=None, help='json to set explicit types for each field, format {<label>:[<col1 type>, <col2 type> ...]} where type can be 0(null),1(bool),2(numeric),3(string)')
 @click.option('--skip-invalid-nodes', '-s', default=False, is_flag=True, help='ignore nodes that use previously defined IDs')
 @click.option('--skip-invalid-edges', '-e', default=False, is_flag=True, help='ignore invalid edges, print an error message and continue loading (True), or stop loading after an edge loading failure (False)')
 
 
-def bulk_insert(graph, host, port, password, nodes, relations, separator, max_token_count, max_buffer_size, max_token_size, quote_minimal, skip_invalid_nodes, skip_invalid_edges):
+def bulk_insert(graph, host, port, password, nodes, relations, separator, max_token_count, max_buffer_size, max_token_size, quote, field_types, skip_invalid_nodes, skip_invalid_edges):
     global CONFIGS
     global NODE_DICT
     global TOP_NODE_ID
     global QUERY_BUF
     global QUOTING
+    global FIELD_TYPES
 
     if sys.version_info[0] < 3:
         raise Exception("Python 3 is required for the RedisGraph bulk loader.")
 
-    if quote_minimal:
-        QUOTING=csv.QUOTE_MINIMAL
-    else:
-        QUOTING=csv.QUOTE_NONE 
+    if field_types != None:
+        try :
+            FIELD_TYPES = json.loads(field_types)
+        except: 
+            raise Exception("Problem parsing field-types. Use the format {<label>:[<col1 type>, <col2 type> ...]} where type can be 0(null),1(bool),2(numeric),3(string) ")
+
+    QUOTING=int(quote)
 
     TOP_NODE_ID = 0 # reset global ID variable (in case we are calling bulk_insert from unit tests)
     CONFIGS = Configs(max_token_count, max_buffer_size, max_token_size, skip_invalid_nodes, skip_invalid_edges)

example2/Robots.csv

@@ -0,0 +1,5 @@
+"name"
+"Beebop"
+"30165"
+"Chappy"
+"Wal-e"