The IPQS Flat File Database Reader brings a new standard of performance and compression to our IP reputation and geolocation services. Setting up and using the reader can be achieved in minutes and provides access to our proxy detection database.
ipqs-db-reader package is available as a "rock" from
You may then open the included documentation, if you wish.
The source code as well as examples for IPv4 and IPv6 lookups are available in our Github repository.
Using our flat file database system to lookup an IP address is simple:
See the table below for the boolean values and default columns included in
most files. Your particular file may have columns in addition to the
default columns. Arbitrary columns may be queried with the
record:get("ColumnName") syntax. Speak with your
IPQualityScore representative if you have any questions about your flat
|Is this IP address suspected to be a proxy? (SOCKS, Elite, Anonymous, VPN, Tor, etc.)
|Is this IP suspected of being a VPN connection? This can include data center ranges which can become active VPNs at any time. The "proxy" status will always be true when this value is true.
|Is this IP suspected of being a TOR connection? This can include previously active TOR nodes and exits which can become active TOR exits at any time. The "proxy" status will always be true when this value is true.
|Is this IP associated with being a confirmed crawler from a mainstream search engine such as Googlebot, Bingbot, Yandex, etc. based on hostname or IP address verification.
|Indicates if bots or non-human traffic has recently used this IP address to engage in automated fraudulent behavior. Provides stronger confidence that the IP address is suspicious.
|This value will indicate if there has been any recently verified abuse across our network for this IP address. Abuse could be a confirmed chargeback, compromised device, fake app install, or similar malicious behavior within the past few days.
|This value will indicate if the IP has been blacklisted by any 3rd party agency for spam, abuse or fraud.
|This value will indicate if the IP is a private, non-routable IP address.
|This value will indicate if the IP is likely owned by a mobile carrier.
|This value will indicate if the IP has recently had open (listening) ports.
|This value will indicate if the IP is likely owned by a hosting provider or is leased to a hosting company.
|Identifies active VPN connections used by popular VPN services and private VPN servers.
|Identifies active TOR exits on the TOR network.
|Indicates if this IP is likely to be a public access point such as a coffee shop, college or library.
|Classification of the IP address connection type as "Residential", "Corporate", "Education", "Mobile", "Data Center", or "Unknown".
|How frequently the IP address is engaging in abuse across the IPQS threat network. Values can be "high", "medium", "low", or "none".
Two character country code of IP address or "N/A" if unknown.
City of IP address if available or "N/A" if unknown.
ISP if one is known. Otherwise "N/A".
Organization if one is known. Can be parent company or sub company of the listed ISP. Otherwise "N/A".
Autonomous System Number if one is known. 0 if nonexistent.
Timezone of IP address if available or "N/A" if unknown.
Latitude of IP address if available or 0.00 if unknown.
Longitude of IP address if available or 0.00 if unknown.
The "strictness" = 0 fraud score for this IP address. See Proxy Detection API Docs for details about our fraud scores and the strictness parameter.
The "strictness" = 1 fraud score for this IP address. See Proxy Detection API Docs for details about our fraud scores and the strictness parameter.
Each database only holds either IPv4 or IPv6 data. Therefore you may need two instances of the reader available depending on your use case.
Due to the nature of IEEE-754 floating-point representation,
floating-point values may display misleading precision. For example,
32.51 when converted to little endian hexadecimal representation is:
`0x3D0A0242`, which if interpreted literally actually represents
32.509998321533 in Lua. Our Latitude and Longitude values should be
interpreted as accurate to 2 (rounded) decimal places, e.g.